R/rt_fund_pmc.R

In rtransparency: Identifies Indicators of Transparency

# TODO: First find the paragraphs that contain intersting words and then
#    apply the obliteration and rest of functions and test if this saves
#    time!



#' Identify mentions of support
#'
#' Identifies mentions of "This work was funded by ..." and of "This work was
#'     completed using funds from ..."
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_support_1 <- function(article) {

  # synonyms <- .create_synonyms()
  # words <- c("This", "research", "is_have", "funded", "by")
  #
  # this_research <-
  #   synonyms %>%
  #   magrittr::extract(words[1:2]) %>%
  #   lapply(.bound) %>%
  #   lapply(.encase) %>%
  #   paste(collapse = " ")
  #
  # synonyms %>%
  #   magrittr::extract(words[3:5]) %>%
  #   lapply(.bound) %>%
  #   lapply(.encase) %>%
  #   # lapply(.max_words) %>%
  #   paste(collapse = synonyms$txt) %>%
  #   paste(this_research, .) %>%
  #   grep(article, perl = TRUE)


  synonyms <- .create_synonyms()
  words <- c("This_singular", "research_singular", "is_singular", "funded_funding", "by")

  this_research <-
    synonyms %>%
    magrittr::extract(words[1:2]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = ".{0,15}")

  was_funded_by <-
    synonyms %>%
    magrittr::extract(words[3:5]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt)

  singular <-
    c(this_research, was_funded_by) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)

  if (!length(singular)) {

    words <- c("These", "researches", "are", "funded_funding", "by")

    synonyms %>%
      magrittr::extract(words) %>%
      lapply(.bound, location = "end") %>%
      lapply(.encase) %>%
      # lapply(.max_words) %>%
      paste(collapse = synonyms$txt) %>%
      grep(article, perl = TRUE)

  } else {

    return(singular)

  }
}


#' Identify mentions of support
#'
#' Returns the index with the elements of interest.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_support_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("funded_funding", "this_singular", "research_singular")

  singular <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound, location = "end") %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)

  if (!length(singular)) {

    words <- c("funded", "these", "researches")

    synonyms %>%
      magrittr::extract(words) %>%
      lapply(.bound, location = "end") %>%
      lapply(.encase) %>%
      # lapply(.max_words) %>%
      paste(collapse = synonyms$txt) %>%
      grep(article, perl = TRUE)

  } else {

    return(singular)

  }
}


#' Identify mentions of support
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_support_3 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("research", "is_have", "funded", "by")

  research_is <-
    synonyms %>%
    magrittr::extract(words[1:2]) %>%
    lapply(.bound, location = "end") %>%
    lapply(.encase) %>%
    paste(collapse = " ")

  synonyms %>%
    magrittr::extract(words[3:4]) %>%
    lapply(.bound, location = "end") %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt) %>%
    paste(research_is, ., sep = synonyms$txt) %>%
    grep(article, perl = TRUE)
}


#' Identify mentions of support
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_support_4 <- function(article) {

  # TODO: I have now made this function much more general than before, by
  #    removing the requirement for "is" from the start. It seems that this now
  #    became more sensitive with no loss in specificity. If it remains so in
  #    further testing, remove all previews functions, which are basically more
  #    specific versions of this!

  synonyms <- .create_synonyms()
  words <- c("funded", "by", "award")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)
}


get_support_5 <- function(article) {

  # TODO: This is trying to capture some phrases missed by get_support_4 b/c of
  #    the requirement for is, e.g. "Project supported by X". I am introducing
  #    a 3 word limit at the start to make it stricter - upon tests, there were
  #    very very few mistakes (1/200 FP) without introducing this restriction.

  synonyms <- .create_synonyms()
  words <- c("funded", "by", "foundation")

  funded_by_award <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt)

  start_of_sentence <- "(^\\s*|(:|\\.)\\s*|[A-Z][a-zA-Z]+\\s*)"
  .max_words(start_of_sentence, n_max = 4, space_first = FALSE) %>%
    paste0(funded_by_award) %>%
    grep(article, perl = TRUE)
}


#' Identify mentions of support
#'
#' Return the index of support statements such as: We gratefully acknowledge
#'     support from the UK Engineering and Physical Sciences Research Council.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_support_6 <- function(article) {

  synonyms <- .create_synonyms()
  words_1 <- c("acknowledge", "support_only", "foundation_award")
  words_2 <- c("support_only", "foundation_award", "acknowledged")

  .max_words(c("acknowledge", "support_only"), 2)

  acknowledge <-
    synonyms %>%
    magrittr::extract(words_1[1]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    lapply(.max_words)

  support_foundation <-
    synonyms %>%
    magrittr::extract(words_1[2:3]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt)

  a <-
    c(acknowledge, support_foundation) %>%
    paste(collapse = " ") %>%
    grep(article, perl = TRUE)

  b <-
    synonyms %>%
    magrittr::extract(words_2) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)

  return(unique(c(a, b)))
}


#' Identify mentions of support
#'
#' Return the index of support statements such as: Support was provided by the
#'     U.S. Department of Agriculture (USDA) Forest Service.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_support_7 <- function(article) {

  synonyms <- .create_synonyms()

  a <- "Support"
  b <- .encase(synonyms$received)
  d <- .encase(synonyms$by)
  grep(paste(a, b, d, sep = synonyms$txt), article, perl = TRUE)

}


#' Identify mentions of support
#'
#' Return the index of support statements such as: We are grateful to the
#'     National Institute of Mental Health (MH091070: PI’s Dante Cicchetti and
#'     Sheree L. Toth) for support of this work.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_support_8 <- function(article) {

  # TODO: This function was onboarded to capture a tiny proportion of
  #    statements. Consider (1) activating this function only if no hit by the
  #    more prevalent functions, (2) placing it in Acknowledgements (all missed
  #    article that prompted this are in the acknowledgements) and (3) to
  #    change this function into processing sentence by sentence to cover for
  #    all permutations of these terms.

  synonyms <- .create_synonyms()
  words <- c("foundation", "provide", "funding_financial", "for_of", "research")
  max_words <- .max_words(" ", n_max = 3, space_first = FALSE)

  # foundation <-
  #   synonyms %>%
  #   magrittr::extract(words[1]) %>%
  #   lapply(.bound) %>%
  #   lapply(.encase)
  #
  # funding_for_research <-
  #   synonyms %>%
  #   magrittr::extract(words[2:4]) %>%
  #   lapply(.bound) %>%
  #   lapply(.encase) %>%
  #   paste(collapse = max_words)
  #
  #   for_research <-
  #     synonyms %>%
  #     magrittr::extract(words[4:5]) %>%
  #     lapply(.bound) %>%
  #     lapply(.encase) %>%
  #     paste(collapse = max_words)

  # c(foundation, funding_for_research) %>%
  #   paste(collapse = synonyms$txt) %>%
  #   grep(article, perl = TRUE)

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)

}


#' Identify mentions of support
#'
#' Return the index of support statements such as: The US Environmental
#'     Protection Agency’s (USEPA) Office of Research and Development funded and
#'     managed the research described in the present study.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_support_9 <- function(article) {

  # Only done for words other than "supported" b/c this is very generic

  synonyms <- .create_synonyms()
  words <- c("foundation", "funded", "research")

  foundation <-
    synonyms %>%
    magrittr::extract(words[1]) %>%
    lapply(.bound) %>%
    lapply(.encase)

  funding <-
    synonyms %>%
    magrittr::extract(words[2]) %>%
    lapply(grep, pattern = "upport", value = TRUE, invert = TRUE) %>%
    lapply(.bound) %>%
    lapply(.encase)

  research <-
    synonyms %>%
    magrittr::extract(words[3]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste0(".{0,20}\\.")  # only match to words at the end of the sentence.

  c(foundation, funding, research) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)

}


#' Identify mentions of support
#'
#' Return the index of support statements such as: We are grateful to the
#'     National Institute of Mental Health (MH091070: PI’s Dante Cicchetti and
#'     Sheree L. Toth) for support of this work.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_support_10 <- function(article) {

  # TODO: This function was onboarded to capture a tiny proportion of
  #    statements. Consider (1) activating this function only if no hit by the
  #    more prevalent functions, (2) placing it in Acknowledgements (all missed
  #    article that prompted this are in the acknowledgements) and (3) to
  #    change this function into processing sentence by sentence to cover for
  #    all permutations of these terms.

  synonyms <- .create_synonyms()
  words <- c("thank", "foundation", "funding_financial", "research")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)

}


#' Identify mentions of developed
#'
#' Return the index of statements such as: This publication was developed
#'     under Assistance Agreement No. 83563701-0 awarded by the U.S.
#'     Environmental Protection Agency to the University of Michigan.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_developed_1 <- function(article) {

  # TODO: This function was onboarded to capture a tiny proportion of
  #    statements. Consider (1) activating this function only if no hit by the
  #    more prevalent functions, (2) placing it in Acknowledgements (all missed
  #    article that prompted this are in the acknowledgements) and (3) to
  #    change this function into processing sentence by sentence to cover for
  #    all permutations of these terms.

  synonyms <- .create_synonyms()
  words <- c("This", "research", "is", "developed", "by", "foundation")

  this_research_is <-
    synonyms %>%
    magrittr::extract(words[1:3]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt)

  developed <- words[4]

  by_foundation <-
    synonyms %>%
    magrittr::extract(words[5:6]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt)

  c(this_research_is, developed, by_foundation) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)

}

#' Identify mentions of received
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_received_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("received", "funds_award_financial", "by")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = " ") %>%
    grep(article, perl = TRUE)
}


#' Identify mentions of received
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_received_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("received", "funding_financial", "by", "foundation_award")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)
}


#' Identify mentions of recepient
#'
#' Returns the index of mentions such as: "Recipient of National Institutes of
#'     Health Grants AG044552, AI121621, and DK112365.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_recipient_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("recipient", "award")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)
}


#' Identify mentions of "the authors ... financial support
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_authors_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("This", "author", "funds_award_financial")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)


  # "[Ss]upport",
  # "[Ff]inancial assistance",
  # "\\b[Aa]id\\b",

  # The authors thank X for funding
  # The authors received no/did not receive financial support
  # The authors acknowledge the/disclosed receipt of support\\b/financial support of
  # The authors are supported by

  # The authors received no funds
  # The authors have no support or funding to report
}


#' Identify mentions of "the authors have no funding ..."
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_authors_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("This", "author", "have", "no", "funding_financial_award")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)
}


#' Identify mentions of "thank ... financial support
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_thank_1 <- function(article) {

  synonyms <- .create_synonyms()

  synonyms$financial <- c(synonyms$financial, "for supporting")
  words <- c("We", "thank", "financial")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)
}


#' Identify mentions of thank you statements
#'
#' Returns the index with the elements related to "The authors acknowledge
#'     Worldwide Cancer Research (AICR) 15-1002; Blueprint 282510; ..."
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_thank_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("thank")

  thank <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt)

  thank %>%
    paste("[0-9]{5}", sep = synonyms$txt) %>%
    grep(article, perl = TRUE)
}


#' Identify mentions of "funding for this study was..."
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_fund_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("funding_financial_award", "for", "research", "received")

  funding_for <-
    synonyms %>%
    magrittr::extract(words[1:2]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = " ")

  synonyms %>%
    magrittr::extract(words[3:4]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    paste(funding_for, ., sep = synonyms$txt) %>%
    grep(article, perl = TRUE)
}


#' Identify mentions of Funding titles
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_fund_2 <- function(article) {

  b <- integer()
  synonyms <- .create_synonyms()
  words <- c("funding_title")

  a <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.title) %>%
    lapply(.encase) %>%
    paste() %>%
    grep(article, perl = TRUE)

  if (!!length(a)) {

    for (i in seq_along(a)) {

      if (is.na(article[a[i] + 1])) {

        b <- c(b, a[i])

      } else {

        if (nchar(article[a[i] + 1]) == 0) {

          b <- c(b, a[i], a[i] + 2)

        } else {

          b <- c(b, a[i], a[i] + 1)

        }
      }
    }

  } else {

    b <-
      synonyms %>%
      magrittr::extract(words) %>%
      lapply(.title, within_text = TRUE) %>%
      lapply(.encase) %>%
      paste() %>%
      grep(article, perl = TRUE)

  }
  return(b)
}


#' Identify mentions of Funding titles
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_fund_3 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("any_title")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.encase) %>%
    paste("[A-Z]") %>%
    grep(article, perl = TRUE)

}


#' Identify mentions of funds in acknowledgements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_fund_acknow <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("funded", "funds_award_financial")

  funded_synonyms <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    unlist()

  c(funded_synonyms, "NIH (|\\()(R|P)[0-9]{2}", "awarded by") %>%
    .encase %>%
    grep(article, perl = TRUE, ignore.case = TRUE)

}


#' Identify mentions of funds in acknowledgements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_fund_acknow_new <- function(article) {

  # Detect funding acknowledged in prose. Requires explicit funding language: a
  # funding verb directed at a funder ("supported/funded/financed by ..."), an
  # institutional "support/funding of/from the ...", a grant or award
  # identifier, or a named award. A bare mention of an institution, the word
  # "support", or "acknowledge" is not enough, because those also appear in
  # competing-interest statements, generic thanks and author affiliations.
  patterns <- c(
    "\\b(fund|support|financ|sponsor)(ed|ing)?\\b(\\s+\\w+){0,3}\\s+(by|through)\\b",
    "\\b(support|funding|grant|grants|funds)\\s+(of|from)\\s+the\\b",
    "\\bgrant(s|ed)?\\b(\\s+\\w+){0,3}\\s+(no\\.?|number|#|from|award|agreement|id|ref)\\b",
    "\\b(fellowship|scholarship|studentship|bursary|stipend|endowment)\\b"
  )

  grep(.encase(patterns), article, perl = TRUE, ignore.case = TRUE)
}


# get_fund_acknow_new <- function(article) {
#
#   synonyms <- .create_synonyms()
#   words <- c("acknowledge")
#
#   a <- synonyms %>%
#     magrittr::extract(words) %>%
#     lapply(.bound) %>%
#     lapply(.encase)
#
#   grep(paste0(a, synonyms$txt, "[0-9]{3,10}"), article, perl = TRUE)
#
# }


#' Identify mentions of "Supported by ..."
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_supported_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("Supported", "by")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = " ") %>%
    paste("[a-zA-Z]+") %>%
    grep(article, perl = TRUE)

}

#' Identify mentions of Financial support titles
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_financial_1 <- function(article) {

  b <- integer()

  synonyms <- .create_synonyms()
  words <- c("financial_title")

  a <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.title) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste() %>%
    grep(article, perl = TRUE)


  if (!!length(a)) {

    for (i in seq_along(a)) {

      if (is.na(article[a[i] + 1])) {

        b <- c(b, a[i])

      } else {

        if (nchar(article[a[i] + 1]) == 0) {

          b <- c(b, a[i], a[i] + 2)

        } else {

          b <- c(b, a[i], a[i] + 1)

        }
      }
    }

  } else {

    b <-
      synonyms %>%
      magrittr::extract(words) %>%
      lapply(.title, within_text = TRUE) %>%
      lapply(.encase) %>%
      # lapply(.max_words) %>%
      paste() %>%
      grep(article, perl = TRUE)

  }
  return(b)
}


#' Identify mentions of Financial support titles followed by specific text
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_financial_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("financial_title", "No")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = " ") %>%
    grep(article, perl = TRUE)

}


#' Identify mentions of Financial support titles followed by specific text
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_financial_3 <- function(article) {


  synonyms <- .create_synonyms()
  words <- c("financial_title", "this", "research")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    # lapply(.max_words) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)

}


#' Identify mentions of Disclosure statements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_disclosure_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("disclosure_title")

  a <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.title) %>%
    lapply(.encase) %>%
    paste(collapse = "|") %>%
    grep(article, perl = TRUE)

  out <- integer()

  if (!!length(a)) {

    for (i in seq_along(a)) {

      if (is.na(article[a[i] + 1])) {

        b <- integer()
        d <- integer()

      } else {

        if (nchar(article[a[i] + 1]) == 0) {
          b <- a[i] + 2
        } else {
          b <- a[i] + 1
        }

        d <-
          synonyms$funding_financial_award %>%
          lapply(.bound) %>%
          paste0(collapse = "|") %>%
          grep(article[b], perl = TRUE)

      }

      if (!!length(d)) out <- c(out, a[i], b)
    }
  }
  return(out)
}


#' Identify mentions of Disclosure statements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_disclosure_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("disclosure_title", "funding_financial_award")

  disclosure <-
    synonyms %>%
    magrittr::extract(words[1]) %>%
    lapply(.title, within_text = TRUE)

  funding <-
    synonyms %>%
    magrittr::extract(words[2]) %>%
    lapply(.bound)

  c(disclosure, funding) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)

}


#' Identify mentions of Financial support titles
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_grant_1 <- function(article) {

  # TODO: This whole function takes a LOT of time to run, but it is ONLY
  #    activated for "(Grant [Ss]ponsor(|s):)|)Contract [Gg]rant
  #    [Ss]ponsor(|s):). Consider replacing it with just this!!!

  b <- integer()

  synonyms <- .create_synonyms()
  words <- c("grant_title")

  a <-
    synonyms %>%
    magrittr::extract(words) %>%
    lapply(.title) %>%
    lapply(.encase) %>%
    paste() %>%
    grep(article, perl = TRUE)


  if (!!length(a)) {

    for (i in seq_along(a)) {

      if (is.na(article[a[i] + 1])) {

        b <- c(b, a[i])

      } else {

        if (nchar(article[a[i] + 1]) == 0) {

          b <- c(b, a[i], a[i] + 2)

        } else {

          b <- c(b, a[i], a[i] + 1)

        }
      }
    }

  } else {

    # This is done to avoid mentions such as "Grant AK, Brown AZ, ..."
    grant <- c("G(?i)rant ", "^[A-Z](?i)\\w+ grant ", "Contract grant ")

    support <-
      synonyms %>%
      magrittr::extract(c("support", "funder")) %>%
      lapply(paste0, "(?-i)") %>%
      lapply(.title, within_text = TRUE) %>%
      unlist()

    b <-
      grant %>%
      lapply(paste0, support) %>%
      unlist() %>%
      .encase() %>%
      grep(article, perl = TRUE)

  }
  return(b)
}


#' Identify mentions of Grant numbers in the Funding/Acknowledgements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_french_1 <- function(article) {

  # This study was financed by... - avoiding specifics b/c of UTF-8 characters
  grep("Cette.*tude.*financ.*par", article, perl = TRUE, ignore.case = TRUE)

}


#' Identify mentions of funding in frence
#'
#' Returns the index of mentions such as: "Remerciements Cette étude a été
#'     financée par l’Institut de Recherches Médicales et d’Etudes des Plantes
#'     Médicinales (IMPM)."
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_project_acknow <- function(article) {

  grep("project (no|num)", article, perl = TRUE, ignore.case = TRUE)

}


#' Get common phrases
#'
#' Returns the index with the elements of interest.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_common_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("no", "funding_financial_award", "is", "received")

  no_funding <-
    synonyms %>%
    magrittr::extract(words[1:2]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = " ")

  was_received <-
    synonyms %>%
    magrittr::extract(words[3:4]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = " ")

  no_funding %>%
    paste(was_received, sep = synonyms$txt) %>%
    grep(article, perl = TRUE)
}



#' Get common phrases
#'
#' Returns the index with the elements of interest.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_common_2 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("No", "funding_financial_award", "received")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)
}


#' Get common phrases
#'
#' Identify statements of the following type: "All authors are required to
#'     disclose all affiliations, funding sources and financial or management
#'     relationships that could be perceived as potential sources of bias. The
#'     authors disclosed none.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_common_3 <- function(article) {

  grep("required to disclose.*disclosed none", article)

}


#' Get common phrases
#'
#' Identify statements of the following type: "There were no external funding
#'     sources for this study"
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_common_4 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("no", "funding_financial_award", "for", "this", "research")

  no_funding <-
    synonyms %>%
    magrittr::extract(words[1:2]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt)

  for_this <-
    synonyms %>%
    magrittr::extract(words[3:4]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = " ")

  research <-
    synonyms %>%
    magrittr::extract(words[5]) %>%
    lapply(.bound) %>%
    lapply(.encase)

  c(no_funding, for_this, research) %>%
    paste(collapse = synonyms$txt) %>%
    grep(article, perl = TRUE)

}


#' Get common phrases
#'
#' Identify statements of the following type: "No specific sources of funding."
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_common_5 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("no", "sources", "funding_financial")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = .max_words(" ", space_first = FALSE)) %>%
    grep(article, perl = TRUE)

}


#' Get explicit grant/support phrases from validation residuals.
#'
#' Identify statements such as "This project was funded by grants...",
#' "Funding was provided by...", and "This study was generously supported by...".
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_common_6 <- function(article) {

  grep(
    paste(
      "\\b(this|the) (project|study|work|research).{0,80}(funded by grants?|funded by|supported by|financed by)\\b",
      "\\bfunding was provided by\\b",
      "\\bfinancial support (was )?provided by\\b",
      "\\bgenerously supported by\\b",
      "\\bfor funding this work through\\b",
      "\\bwas supported by .{0,80}(award|fund|grant|foundation|program|programme|project no|project number)\\b",
      sep = "|"
    ),
    article,
    perl = TRUE,
    ignore.case = TRUE
  )

}


#' Identify mentions of "Acknowledgement and"
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_acknow_1 <- function(article) {

  b <- integer()

  txt_0 <- "^Acknowledg(|e)ment(|s)"
  txt_1 <- "(of|and)"
  txt_2 <- "([Ss]upport |\\b[Ff]unding|\\b[Ff]inancial)"

  total_txt <- c(txt_0, txt_1, txt_2)
  indicator_regex <- paste0(total_txt, collapse = " ")

  a <- grep(indicator_regex, article, perl = TRUE, ignore.case = TRUE)

  if (!!length(a)) {

    for (i in seq_along(a)) {

      if (is.na(article[a[i] + 1])) {

        b <- c(b, a)

      } else {

        if (nchar(article[a[i] + 1]) == 0) {

          b <- c(b, a[i], a[i] + 2)

        } else {

          b <- c(b, a[i], a[i] + 1)
        }

      }
    }
  }
  return(b)
}


#' Identify acknowledgements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
get_acknow_2 <- function(article) {

  txt_0 <- "(^A(?i)cknowledg(|e)ment(|s)(?-i))"

  txt_1 <- "(^Acknowledg(|e)ment(|s)"
  txt_2 <- "(of|and)"
  txt_3 <- "([Ss]upport |\\b[Ff]unding|\\b[Ff]inancial))"

  total_txt <- c(txt_1, txt_2, txt_3)
  indicator_regex <- paste0(total_txt, collapse = " ")
  indicator_regex <- paste(txt_0, indicator_regex, sep = "|")

  grep(indicator_regex, article, perl = TRUE)
}


#' Identify funding titles using XML labels
#'
#' Extract XML titles related to funding and all text children.
#'
#' @param article_xml The text as an xml_document.
#' @return The title and its related text as a string.
#' @noRd
.get_fund_pmc_title <- function(article_xml) {

  synonyms <- .create_synonyms()
  b <- ""

  fund_titles <- .encase(synonyms$any_title)

  # back_xpath <-
  #   c(
  #     "back/ack//*[self::title or self::bold or self::italic]",
  #     "back/fn-group//*[self::title or self::bold or self::italic]",
  #     "back/notes//*[self::title or self::bold or self::italic]"
  #   ) %>%
  #   paste(collapse = " | ")

  # If I had not stripped the d1 namespace:
  # "back//fn-group//*[self::d1:title or self::d1:bold or self::d1:italic]"
  # back_matter <-
  #   article_xml %>%
  #   xml2::xml_find_all(back_xpath)

  back_matter <-
    article_xml %>%
    xml2::xml_find_all("back/*[not(name()='ref-list')]") %>%
    xml2::xml_find_all(".//*[self::title or self::bold or self::italic or self::sup]")

  a <-
    back_matter %>%
    xml2::xml_text() %>%
    stringr::str_which(fund_titles)

  if (!!length(a)) {

    b <-
      back_matter %>%
      magrittr::extract(a) %>%
      xml2::xml_parent() %>%
      xml2::xml_contents() %>%
      xml2::xml_text() %>%
      paste(collapse = " ")

    return(b)
  }


  front_matter <-
    article_xml %>%
    xml2::xml_find_all("front//fn//*[self::title or self::bold or self::italic or self::sup]")

  a <-
    front_matter %>%
    xml2::xml_text() %>%
    stringr::str_which(fund_titles)

  if (!!length(a)) {

    b <-
      front_matter %>%
      magrittr::extract(a) %>%
      xml2::xml_parent() %>%
      xml2::xml_contents() %>%
      xml2::xml_text() %>%
      paste(collapse = " ")

    return(b)
  }


  fund_titles <- .title_strict(synonyms$any_title) %>% .encase()

  body_matter <-
    article_xml %>%
    xml2::xml_find_all("body/sec//*[self::title or self::bold or self::italic or self::sup]")

  a <-
    body_matter %>%
    xml2::xml_text() %>%
    stringr::str_which(fund_titles)

  if (!!length(a)) {

    b <-
      body_matter %>%
      magrittr::extract(a) %>%
      xml2::xml_parent() %>%
      xml2::xml_contents() %>%
      xml2::xml_text() %>%
      paste(collapse = " ")

    return(b)
  }

  return(b)
}


#' Identify funding group elements in NLM XML files
#'
#' Identify and return the text found in funding group elements of NLM XML
#'     files. Articles with funding-group elements also have a Funding
#'     indication on PubMed.
#'
#' @param article_xml An NLM XML as an xml_document.
#' @return The text of interest as a list indicating whether it was found, the
#'     quoated institutes and the actual statement as a string.
#' @noRd
.get_fund_pmc_group <- function(article_xml) {

  fund_pmc <- list(
    is_fund_group_pmc = FALSE,
    fund_statement_pmc = "",
    fund_institute_pmc = "",
    fund_source_pmc = ""
  )

  fund_group <-
    article_xml %>%
    xml2::xml_find_all("//funding-group | //award-group | //support-group")

  if (!!length(fund_group)) {

    fund_pmc$is_fund_group_pmc <- TRUE

    fund_pmc$fund_statement_pmc <-
      fund_group %>%
      xml2::xml_find_all(".//funding-statement") %>%
      xml2::xml_text() %>%
      paste(collapse = "")

    fund_pmc$fund_institute_pmc <-
      fund_group %>%
      xml2::xml_find_all(".//institution") %>%
      xml2::xml_contents() %>%
      xml2::xml_text() %>%
      paste(collapse = "; ")

    fund_pmc$fund_source_pmc <-
      fund_group %>%
      xml2::xml_find_all(".//funding-source") %>%
      xml2::xml_text() %>%
      paste(collapse = "; ")
  }


  if (with(fund_pmc, fund_institute_pmc == "N/A" & fund_statement_pmc == "")) {

    fund_pmc$is_fund_group_pmc <- FALSE

  }

  return(fund_pmc)
}


#' Identify funding source elements in NLM XML files
#'
#' Identify and return the text found in funding source elements of NLM XML
#'     files. Some funding sources are not included in the funding-group
#'     elements - these are the sources that this function attempts to capture.
#'     It seems that all articles with such elements also have a Funding
#'     statement on PubMed.
#'
#' @param article_xml An NLM XML as an xml_document.
#' @return The text of interest as a string.
#' @noRd
.get_fund_pmc_source <- function(article_xml) {

  article_xml %>%
    xml2::xml_find_all("body//funding-source | back//funding-source") %>%
    xml2::xml_contents() %>%
    xml2::xml_text() %>%
    paste(collapse = "; ")

}


#' Avoid disclosures that are in fact COI statements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
negate_disclosure_1 <- function(article) {

  synonyms <- .create_synonyms()

  txt <- "[a-zA-Z0-9\\s,()-]*"  # order matters

  disclose_synonyms <- c(
    "[Dd]isclose(|s)(|:|\\.)",
    "[Dd]isclosure(|s)(|:|\\.)"
  )

  conflict_synonyms <- c(
    "conflict(|s) of interest",
    "conflicting interest",
    "conflicting financial interest",
    "conflicting of interest",
    "conflits d'int",
    "conflictos de Inter"
  )

  compete_synonyms <- c(
    "competing interest",
    "competing of interest",
    "competing financial interest"
  )

  and_synonyms <- c(
    "and",
    "&",
    "or"
  )

  not_synonyms <- c(
    "not"
  )

  funded_synonyms <- c(
    "\\bfunded",
    "\\bfinanced",
    "\\bsupported",
    "\\bsponsored",
    "\\bresourced"
  )

  disclose <- .encase(disclose_synonyms)
  conflict <- .encase(c(conflict_synonyms, compete_synonyms))
  and <- .encase(and_synonyms)
  not <- .encase(not_synonyms)
  funded <- .encase(funded_synonyms)

  regex <- paste(disclose, conflict, and, not, funded, sep = txt)
  a <- grepl(regex, article, perl = TRUE)

  if (any(a)) {

    return(a)

  } else {

    funded <- .encase(c(funded_synonyms, synonyms$funding))
    regex <- paste(disclose, funded, conflict, sep = txt)
    grepl(regex, article, perl = TRUE)
  }
}


#' Identify "Funding disclosure. Nothing to declare" statements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
negate_disclosure_2 <- function(article) {

  synonyms <- .create_synonyms()

  Disclosure_synonyms <- c(
    "F(?i)inancial disclosure(|s)(?-i)(|:|\\.)",
    "F(?i)inancial declaration(|s)(?-i)(|:|\\.)",
    "Disclosure(|:|\\.)",
    "Declaration(|:|\\.)"
  )

  disclosure_synonyms <- c(
    "financial disclosure(|s)",
    "financial declaration(|s)",
    "disclosure",
    "declaration"
  )

  disclose_synonyms <- c(
    "to disclose",
    "to declare",
    "to report"
  )

  Disclosure <- .encase(Disclosure_synonyms)
  disclosure <- .encase(disclosure_synonyms)
  disclose   <- .encase(disclose_synonyms)
  no <- .encase(synonyms$No)
  no_1 <- "(no|not have any)"
  no_2 <- "(nil|nothing)"

  regex_1 <-  # Financial disclosure: Nothing to declare
    paste(Disclosure, no) %>%
    .encase()
  regex_2 <-  # Financial disclosure: The authors have no financial disclosures
    paste(Disclosure, paste(no_1, disclosure), sep = synonyms$txt) %>%
    .encase()
  regex_3 <-  # Financial disclosure: The author has nothing to declare
    paste(Disclosure, paste(no_2, disclose), sep = synonyms$txt) %>%
    .encase()

  regex <- paste(regex_1, regex_2, regex_3, sep = "|")
  grepl(regex, article, perl = TRUE)

}


#' Avoid financial that is part of COI statements
#'
#' Returns the index with the elements of interest. More generic than _1.
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
negate_conflict_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("conflict_title")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.title) %>%
    lapply(.encase) %>%
    paste() %>%
    grepl(article, perl = TRUE)
}


#' Avoid mentions that no funding information was provided
#'
#'
#' @param article A List with paragraphs of interest.
#' @return The index of the paragraph of interest.
#' @noRd
negate_absence_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("No", "info", "of", "funding_financial_award", "is", "received")

  synonyms %>%
    magrittr::extract(words) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    grepl(article, perl = TRUE) |
    grepl(
      paste(
        "\\bfunding\\s*:?\\s*none\\b",
        "\\bfinancial support\\s*:?\\s*none declared\\b",
        "\\bno (external |specific |direct )?(funding|financial support|grant support)\\b",
        "\\bno funding and conflicts? of interest\\b",
        "\\bfinancial disclosures?\\s*:?\\s*none\\b",
        "\\bgrant support\\s*&\\s*financial disclosures?\\s*:?\\s*none\\b",
        "\\bno financial support was received\\b",
        "\\breceived no (specific )?(funding|financial support|grant support)\\b",
        "\\bdid not receive any specific grant\\b",
        "\\b(funding\\s*)?(nil|none|n/?a|not applicable)\\.?\\s*$",
        "\\bfunding\\s+(the )?(authors?|author) (have|has) nothing to report\\b",
        "\\b(the )?(authors?|author) (have|has) nothing to report\\b",
        "\\bfunding\\s+nothing to (declare|report)\\b",
        "\\bnothing to (declare|report)\\b",
        "^\\s*(funding|funding statement|funding information|financial support|funding/support|sources of support)\\s*:?\\s*$",
        "\\bfunding acquisition\\b",
        "\\b(study|case report|paper|article|work|research) (was|is) not funded\\b",
        "\\bnot funded\\b",
        "\\bnot receive any funding\\b",
        "\\bdid not receive (any )?funding\\b",
        "\\bhas not been funded\\b",
        "\\bwithout any funding or sponsorship\\b",
        "\\bno financial support is available\\b",
        "\\bno specific funding sources? for this study\\b",
        "\\bfunding/support\\s*none\\b",
        "\\bfunding/support\\s*:?\\s*$",
        "\\bno relevant sources? of funding\\b",
        "\\brelevant sources? of funding pertaining\\b",
        "\\bsupported by (the |this |available |clinical |previous |prior |published )?(evidence|data|analyses|analysis|results|findings|studies|literature|guidelines|imaging|histopathologic|framework|method|model|theory|hypothesis|observations|physiological compatibility|systematic review|contextual factors|recommendation|clinical experience)\\b",
        "\\bresearch supports\\b",
        "\\binformed consent was granted\\b",
        "\\bwaiver of informed consent was granted\\b",
        "\\bethic(al)? approval was granted\\b",
        "\\bapproval was granted\\b",
        "\\bpermission .{0,80} was granted\\b",
        "\\bsupported by psm analysis\\b",
        "\\bsupported by roc analysis\\b",
        "\\b(findings|results|observations) .{0,80}supported by the work of\\b",
        "\\bstructurally supported by\\b",
        "\\bno grants? (were )?involved( in supporting)?\\b",
        "\\bdeclared that no grants? were involved\\b",
        "\\bno external finding\\b",
        "\\bno funders? to report\\b",
        "\\bno competing financial interests?.{0,80}external funding\\b",
        # No-funding boilerplate not covered above: "received no specific grant
        # from any funding agency", "no funds, grants or other support were
        # received", "did not receive funds", "have not received any funding".
        "\\bno specific grant\\b",
        # BMJ standard no-funding declaration: "The authors have not declared a
        # specific grant for this research from any funding agency in the public,
        # commercial or not-for-profit sectors." The funding section is titled
        # "Funding" yet declares absence, so it otherwise leaks through the title route.
        "\\bnot declared a specific grant\\b",
        "\\bno funds,?\\s*(grants?|or other support)",
        "\\b(did|do|does|have|has|had) not receive(d)? any (external |specific |dedicated )?(funding|funds|financial support|grant)",
        "\\bno funds (have been|have not been|were|was) received\\b",
        "\\b(did|do|does) not receive (any )?funds\\b",
        "\\b(have|has|had) not received any (funding|funds|financial)\\b",
        # "(This study/work) was not supported by any funding" — a funding
        # section can be titled "Funding" yet declare the absence of funding,
        # which otherwise leaks through the funding-title route.
        "\\bnot (financially )?supported by any (funding|grant|financial|institution|organi[sz]ation|source|sponsor)",
        # Further no-funding phrasings: "no source(s) of support", "no external
        # sources of funding", "without (the receipt of) any grant/financial support".
        "\\bno sources? of (support|funding|financial support)\\b",
        "\\bno external sources? of funding\\b",
        "\\bwithout (the )?(receipt of )?any (dedicated )?(grant|financial support|funding|external support|sponsorship)",
        # Non-English no-funding declarations (Portuguese/Spanish). The "." class
        # matches the accented characters without putting non-ASCII in the source.
        "\\bn.o (teve|houve|recebeu|recebemos|obteve|reportam|reportaram|relatam|relataram|declaram|possui|possuem) .{0,40}financiamento",
        "\\bsem financiamento\\b",
        "\\bno recibi. .{0,25}financiaci.n",
        "\\bsin .{0,15}financiaci.n",
        "\\bno (hubo|tuvo|existi.|cont.) .{0,30}(financiaci.n|fuentes de financ|apoyo financiero)",
        "\\bno se recibi. .{0,25}financ",
        # French no-funding declarations.
        "\\bsans (le |la )?(financement|subvention|soutien financier|aide financiere)",
        "\\baucun(e)? (financement|subvention|soutien financier|aide financiere|source de financement)",
        "\\bn.a (pas )?(re.u|b.n.fici.) .{0,30}(financement|subvention|soutien)",
        "\\bsans subvention (externe|particuliere)?",
        # German no-funding declarations.
        "\\bkeine? .{0,15}(f.rderung|finanzierung|finanzielle unterstutzung|drittmittel|geldgeber)",
        "\\bohne .{0,15}(f.rderung|finanzielle unterstutzung|finanzierung|drittmittel)",
        "\\b(wurde|wurden) nicht (.{0,12} )?(gef.rdert|finanziert)",
        # Italian no-funding declarations.
        "\\bnessun (.{0,12} )?(finanziamento|sovvenzione|sostegno|supporto economic|fonte di finanziamento)",
        "\\bnon (ha|e stato|hanno|sono stati) ricevut. .{0,20}(finanziament|sovvenzion|sostegno)",
        "\\bsenza (alcun )?(finanziamento|sovvenzione|sostegno|supporto economic)",
        sep = "|"
      ),
      article,
      perl = TRUE,
      ignore.case = TRUE
    )

}


#' Identify explicit positive funding language.
#'
#' Used to avoid treating mixed statements such as "funded by X and receives no
#' funding from industry" as no-funding statements.
#'
#' @param article A List with paragraphs of interest.
#' @return Logical vector.
#' @noRd
has_positive_fund_text <- function(article) {

  grepl(
    paste(
      "\\bfunded by\\b",
      "\\bfunding (for|was) .{0,120}provided by\\b",
      "\\bfunding for .{0,120}(provided|funded)\\b",
      "\\bsupported by .{0,120}(grant|award|foundation|institute|program|programme|project no|project number)\\b",
      "\\bgrant nos?\\b",
      "\\bgrant no\\b",
      "\\bsponsored by\\b",
      sep = "|"
    ),
    article,
    perl = TRUE,
    ignore.case = TRUE
  )

}


#' Allow text scanning after uninformative or contradictory funding tags.
#'
#' @param article A List with paragraphs of interest.
#' @return Logical vector.
#' @noRd
can_scan_after_negated_fund_tag <- function(article) {

  grepl(
    paste(
      "^\\s*(funding|funding statement|funding information|financial support|funding/support|sources of support)\\s*:?\\s*$",
      "^\\s*(funding|financial support|funding/support)\\s*:?\\s*(none|nil|n/?a|not applicable)\\.?\\s*$",
      "^\\s*funding acquisition\\b",
      "^\\s*funding\\s*:?\\s*the authors received no specific funding",
      "\\breceived no financial support for the research",
      sep = "|"
    ),
    article,
    perl = TRUE,
    ignore.case = TRUE
  )

}


#' Remove mentions of COIs that may cause FPs
#'
#' Returns the text without potentially misleading mentions of COIs.
#'
#' @param article A List with paragraphs of interest.
#' @return The list of paragraphs without mentions of financial COIs.
#' @noRd
obliterate_conflict_1 <- function(article) {

  # Good for finding, but not for substituting b/c it's  a lookahead
  # words <- c(
  #   # positive lookahead makes these phrases interchangeable
  #   "(?=[a-zA-Z0-9\\s,()-]*(financial|support))",
  #   "(?=[a-zA-Z0-9\\s,()-]*(conflict|competing))"
  # )

  synonyms <- .create_synonyms()

  financial_1 <- "(funding|financial|support)"
  financial_2 <- "(financial|support)"
  relationship <- synonyms$relationship %>% .bound() %>% .encase()
  conflict <- synonyms$conflict %>% .bound() %>% .encase()
  financial_interest <- "(financial(?:\\s+\\w+){0,3} interest)"

  regex_1 <- paste(financial_1, relationship, conflict, sep = synonyms$txt)
  regex_2 <- paste(conflict, relationship, financial_2, sep = synonyms$txt)
  regex_3 <- paste(relationship, financial_interest,  sep = synonyms$txt)

  c(regex_1, regex_2, regex_3) %>%
    lapply(.encase) %>%
    paste(collapse = "|") %>%
    gsub("", article, perl = TRUE)

}


#' Remove COI statements with language relevant to funding statements
#'
#' Returns the text without mentions of COIs related to industry ties.
#'
#' @param article A List with paragraphs of interest.
#' @return The list of paragraphs without mentions of financial COIs.
#' @noRd
obliterate_conflict_2 <- function(article) {

  # Good for finding, but not for substituting b/c it's  a lookahead
  # words <- c(
  #   # positive lookahead makes these phrases interchangeable
  #   "(?=[a-zA-Z0-9\\s,()-]*(financial|support))",
  #   "(?=[a-zA-Z0-9\\s,()-]*(conflict|competing))"
  # )
  synonyms <- .create_synonyms()

  industry_words <- c(
    "\\bconsult(ant|ing)\\b",
    "\\bhonorari(um|a)\\b",
    "\\bspeaker\\b",
    "\\badvisory board(|s)\\b",
    "\\bfee(|s)\\b"
  )

  industry_words %>%
    .encase %>%
    paste("(\\.|;|$)", sep = ".*") %>%
    gsub("", article, perl = TRUE, ignore.case = TRUE)

}


#' Remove author conflict-of-interest disclosures phrased as funding
#'
#' Sports-medicine journals (AOSSM titles such as AJSM and OJSM) introduce
#'     author conflict-of-interest disclosures with a fixed preamble, "One or
#'     more of the authors has declared the following potential conflict of
#'     interest or source of funding:". The relationships that follow
#'     ("received research support from <company>", royalties, consultancy,
#'     speaking fees) are industry ties of the authors, not funding for the
#'     study, but their "received research support from ..." wording is read as
#'     a funding acknowledgment. The disclosure clause is removed from this
#'     preamble onward so it no longer registers as funding; a separate Funding
#'     statement elsewhere in the article is unaffected.
#'
#' @param article A List with paragraphs of interest.
#' @return The list of paragraphs without the conflict-of-interest disclosure.
#' @noRd
obliterate_conflict_3 <- function(article) {

  gsub(
    "conflict of interest or source of funding.*",
    "", article, perl = TRUE, ignore.case = TRUE
  )

}


#' Remove mentions of commonly misleading funding statements
#'
#' Returns the text without potentially misleading phrases related to funding.
#'
#' @param article A List with paragraphs of interest.
#' @return The list of paragraphs without mentions of financial COIs.
#' @noRd
obliterate_misleading_fund_1 <- function(article) {

  misleading_fund <- c(
    "travel grant(|s)",
    "grant(|s) for travel(|ling)",
    "Fund(|s) Collection",
    # Open-access publishing arrangements ("Open access funding enabled and
    # organized by Projekt DEAL / CAUL / IReL", "Open access funding provided
    # by ...") pay the article-processing charge; they are not research
    # funding. Strip the funding noun phrase so the "funding ... by <consortium>"
    # clause stops registering as a funding acknowledgment. Any genuine grant in
    # the same statement keeps its own "funded/supported by ..." wording.
    # gsub here is case-sensitive, so the leading words carry explicit classes.
    "[Oo]pen[ -]?[Aa]ccess [Ff]unding",
    "[Oo]pen[ -]?[Aa]ccess [Pp]ubl(ication|ishing) [Ff]unding",
    "[Oo]pen[ -]?[Aa]ccess [Pp]ubl(ication|ishing)( [Cc]harges?| [Ff]ees?| [Cc]osts?)"
  )

  misleading_fund %>%
    .encase %>%
    gsub("", article, perl = TRUE)

}



#' Remove disclosures with inappropriate sentences
#'
#' Returns the text without potentially misleading disclsoures mentions. This
#'     is intended to solve problems with disclosures, such as: Disclosure.
#'     Authors have no conflict of interests, and the work was not supported
#'     or funded by any drug company. This project was funded by the Deanship
#'     of Scientific Research, King Abdulaziz University, Jeddah, Saudi Arabia
#'     (Grant No. 4/165/1431);
#'
#' @param article A List with paragraphs of interest.
#' @return The list of paragraphs without mentions of financial COIs.
#' @noRd
obliterate_disclosure_1 <- function(article) {

  synonyms <- .create_synonyms()
  words <- c("disclosure_title", "conflict", "and", "not", "funded")

  # disclosure_title <-
  #   synonyms %>%
  #   magrittr::extract(words[1]) %>%
  #   lapply(.title) %>%
  #   lapply(.encase)

  synonyms %>%
    magrittr::extract(words[2:5]) %>%
    lapply(.bound) %>%
    lapply(.encase) %>%
    paste(collapse = synonyms$txt) %>%
    paste0(synonyms$txt, ., synonyms$txt, "($|.)") %>%
    gsub("", article, perl = TRUE)

  # disclosure_title %>%
  #   paste0("(", synonyms$txt, conflict_funded, synonyms$txt, ")") %>%
  #   gsub("\\1", article, perl = TRUE)


  # if (any(a)) {
  #
  #   return(a)
  #
  # } else {
  #
  #   funded <- .encase(c(funded_synonyms, synonyms$funding))
  #   regex <- paste(disclose, funded, conflict, sep = txt)
  #   grepl(regex, article, perl = TRUE)
  # }

}


#' Identify non-English (multilingual) positive funding statements.
#'
#' Detects funding RECEIVED in Spanish, Portuguese, French, German and Italian,
#' matched on transliterated (accent-stripped) text. No-funding declarations are
#' removed downstream by negate_absence_1. Each alternative requires a positive
#' funding context (a funder preposition or verb), and the tokens are
#' language-distinctive, so this does not fire on English text.
#'
#' @param article The text as a vector of strings.
#' @return Index of elements with phrase of interest
#' @noRd
.which_multilingual_fund_1 <- function(article) {

  grep(
    paste(
      # Spanish / Portuguese
      "financiad[oa]s? (por|pel|a traves|mediante|con|gracias|atraves)",
      "(foi|fue|sido) financiad",
      "recib[io]{2}.{0,20}(beca|subvenci|financiaci|ayuda economica|apoyo financ)",
      "\\bbeca de (salud|investiga|estudio|doctorado|posgrado|la )",
      "\\bbolsa de (estudo|pesquisa|doutorado|mestrado|iniciacao)",
      "subvencion(ado|es)? (de|del|por|otorgad)",
      "patrocin(io|ado|ada) (de|del|por|educativo|sin restricciones)",
      "apoyo (financiero|economico) (de|del)",
      "apoio financeiro (d|por)",
      "auxilio (a pesquisa|financeiro)",
      # French
      "finance{1,2}s? par (le|la|les|l|un|une)",
      "\\bsubvention (de|du|des|accordee|allouee)",
      "\\bbourse (de|d|du)",
      "soutien financier (de|du|des|d)",
      # German
      "gef.rdert (von|vom|durch|im rahmen|mit mitteln)",
      "f.rderung (durch|von|des|im rahmen)",
      "finanziert (von|vom|durch|aus|mit)",
      "finanzielle unterstutzung (durch|von|des)",
      "\\bdrittmittel",
      "f.rderkennzeichen",
      # Italian
      "finanziat[oa] (da|dal|dalla|con|grazie|nell)",
      "\\bfinanziamento (di|del|della|da|pubblico|ricevuto)",
      "sovvenzion(e|ato) (di|del|da)",
      "borsa di studio",
      "supporto (finanziario|economico) (di|del|da)",
      "con il (supporto|contributo) (finanziario|economico|non condizionante|incondizionato).{0,8}di",
      "grazie al (supporto|contributo|finanziamento)",
      sep = "|"
    ),
    article,
    perl = TRUE,
    ignore.case = TRUE
  )

}


#' Identify and extract Funding statements in PMC XML files.
#'
#' Takes a PMC XML file as a list of strings and returns data related to the
#'     presence of a Funding statement, including whether a Funding statement
#'     exists. If a Funding statement exists, it extracts it. This is a modified
#'     version of the `rt_fund_pmc` designed for integration with `rt_all_pmc`.
#'
#' @param article_ls A PMC XML as a list of strings.
#' @param pmc_fund_ls A list of results from the `.get_fund_pmc` function.
#' @return A dataframe of results.
#' @noRd
.rt_fund_pmc <- function(article_ls, pmc_fund_ls) {

  # TODO Update to match format of rt_coi_pmc.

  index <- integer()

  # Way faster than index_any[["reg_title_pmc"]] <- NA
  index_any <- list(
    support_1 = NA,
    support_3 = NA,
    support_4 = NA,
    support_5 = NA,
    support_6 = NA,
    support_7 = NA,
    support_8 = NA,
    support_9 = NA,
    support_10 = NA,
    developed_1 = NA,
    received_1 = NA,
    received_2 = NA,
    recipient_1 = NA,
    authors_1 = NA,
    authors_2 = NA,
    thank_1 = NA,
    thank_2 = NA,
    fund_1 = NA,
    fund_2 = NA,
    fund_3 = NA,
    supported_1 = NA,
    financial_1 = NA,
    financial_2 = NA,
    financial_3 = NA,
    grant_1 = NA,
    french_1 = NA,
    common_1 = NA,
    common_2 = NA,
    common_3 = NA,
    common_4 = NA,
    common_5 = NA,
    common_6 = NA,
    acknow_1 = NA,
    disclosure_1 = NA,
    disclosure_2 = NA
  )

  index_ack <- list(
    fund_ack = NA,
    fund_ack_new = NA,
    project_ack = NA
  )

  relevance_ls <- list(
    is_relevant_fund = NA
  )

  out <- list(
    is_fund_pred = FALSE,
    fund_text = "",
    is_explicit_fund = NA
  )

  # True only if a fund-statement or fund-title was found
  if (pmc_fund_ls$is_fund_pred) {

    out$is_fund_pred <- TRUE
    out$fund_text <- pmc_fund_ls$fund_text

    is_absent <- negate_absence_1(out$fund_text) &&
      !has_positive_fund_text(out$fund_text)

    if (is_absent) {
      out$is_fund_pred <- FALSE
      out$fund_text <- ""
      out$is_explicit_fund <- NA
      if (!can_scan_after_negated_fund_tag(pmc_fund_ls$fund_text)) {
        return(c(relevance_ls, out, index_any, index_ack))
      }
    }

    if (out$is_fund_pred && !is.na(pmc_fund_ls$is_fund_pmc_title)) {

      out$is_explicit_fund <- TRUE

    }

    if (out$is_fund_pred) {
      return(c(relevance_ls, out, index_any, index_ack))
    }
  }

  # TODO Consider adding unique
  article <-
    article_ls[c("ack", "body", "footnotes")] %>%
    unlist() # %>%
  # unique()


  # Check relevance
  # TODO Consider adding Department
  fund_regex <- "fund|support|financ|receive|grant|none|sponsor|fellowship|Association|Institute|National|Foundation|finanz|f.rder|gef.rder|subvention|beca|bolsa|fomento|patrocin|aux.lio|sovvenzion|borsa|drittmittel"
  article %<>% purrr::keep(~ stringr::str_detect(.x, stringr::regex(fund_regex, ignore_case = TRUE)))

  relevance_ls$is_relevant_fund <- !!length(article)

  # Check for relevance
  if (!relevance_ls$is_relevant_fund) {

    return(c(relevance_ls, out, index_any, index_ack))

  }


  article_processed <-
    article %>%
    iconv(from = 'UTF-8', to = 'ASCII//TRANSLIT', sub = "") %>%   # keep first
    trimws() %>%
    .obliterate_fullstop_1() %>%
    .obliterate_semicolon_1() %>%  # adds minimal overhead
    .obliterate_comma_1() %>%   # adds minimal overhead
    .obliterate_apostrophe_1() %>%
    .obliterate_punct_1() %>%
    .obliterate_line_break_1() %>%
    obliterate_conflict_1() %>%
    obliterate_conflict_2() %>%
    obliterate_conflict_3() %>%
    obliterate_disclosure_1() %>%   # Adds 30s overhead!
    obliterate_misleading_fund_1()


  # Identify sequences of interest
  index_any$support_1 <- get_support_1(article_processed)
  index_any$support_3 <- get_support_3(article_processed)
  index_any$support_4 <- get_support_4(article_processed)
  index_any$support_5 <- get_support_5(article_processed)
  index_any$support_6 <- get_support_6(article_processed)
  index_any$support_7 <- get_support_7(article_processed)
  index_any$support_8 <- get_support_8(article_processed)
  index_any$support_9 <- get_support_9(article_processed)
  index_any$support_10 <- get_support_10(article_processed)
  index_any$developed_1 <- get_developed_1(article_processed)
  index_any$received_1 <- get_received_1(article_processed)
  index_any$received_2 <- get_received_2(article_processed)
  index_any$recipient_1 <- get_recipient_1(article_processed)
  index_any$authors_1 <- get_authors_1(article_processed)
  index_any$authors_2 <- get_authors_2(article_processed)
  index_any$thank_1 <- get_thank_1(article_processed)
  index_any$thank_2 <- get_thank_2(article_processed)
  index_any$fund_1 <- get_fund_1(article_processed)
  index_any$fund_2 <- get_fund_2(article_processed)
  index_any$fund_3 <- get_fund_3(article_processed)
  index_any$supported_1 <- get_supported_1(article_processed)
  index_any$financial_1 <- get_financial_1(article_processed)
  index_any$financial_2 <- get_financial_2(article_processed)
  index_any$financial_3 <- get_financial_3(article_processed)
  index_any$grant_1 <- get_grant_1(article_processed)
  index_any$french_1 <- get_french_1(article_processed)
  index_any$common_1 <- get_common_1(article_processed)
  index_any$common_2 <- get_common_2(article_processed)
  index_any$common_3 <- get_common_3(article_processed)
  index_any$common_4 <- get_common_4(article_processed)
  index_any$common_5 <- get_common_5(article_processed)
  index_any$common_6 <- get_common_6(article_processed)
  index_any$acknow_1 <- get_acknow_1(article_processed)
  index_any$disclosure_1 <- get_disclosure_1(article_processed)
  index_any$disclosure_2 <- get_disclosure_2(article_processed)

  index <- unlist(index_any) %>% unique() %>% sort()

  # Add non-English (multilingual) positive-funding matches. No-funding
  # declarations among them are removed by negate_absence_1 below.
  index <- union(index, .which_multilingual_fund_1(article_processed)) %>%
    sort()

  # Remove potential mistakes
  if (!!length(index)) {

    # Funding info can be within COI statements, as per Ioannidis
    # Comment out until problems arise
    # if (length(unlist(index_any[c("authors_2")]))) {
    #   is_coi <- negate_conflict_1(article_processed[min(index) - 1])
    #   index <- index[!is_coi]
    # }


    # Difficult to make it work properly because it does not
    # disclosures <- c("disclosure_1", "disclosure_2")
    # if (!!length(unlist(index_any[disclosures]))) {
    #
    #   for (i in seq_along(disclosures)) {
    #
    #     ind <- index_any[[disclosures[i]]]
    #     is_coi_disclosure <- negate_disclosure_1(article_processed[ind])
    #     index_any[[disclosures[i]]] <- ind[!is_coi_disclosure]
    #
    #   }
    #
    # }

    is_absent <- negate_absence_1(article_processed[index])
    index <- index[!is_absent]

    # Currently removed b/c I made the disclosure functions more robust to
    #     statements like "Financial disclosure. Nothing to disclose.
    # disclosures <- unique(unlist(index_any[c("disclosure_1", "disclosure_2")]))
    # if (!!length(disclosures)) {
    #
    #   if (length(disclosures) == 1) {
    #
    #     is_disclosure <- negate_disclosure_2(paragraphs[index])
    #     index <- index[!is_disclosure]
    #
    #   } else {
    #
    #     disclosure_text <- paste(article_processed[disclosures], collapse = " ")
    #     is_disclosure <- negate_disclosure_2(disclosure_text)
    #     index <- setdiff(index, disclosures)
    #
    #   }
    # }
  }

  if (!!length(index)) {

    out$is_explicit_fund <- !!length(unlist(index_any))
    out$is_fund_pred <- !!length(index)
    out$fund_text <- article[index] %>% paste(collapse = " ")

    index_any %<>% purrr::map(function(x) !!length(x))

    return(c(relevance_ls, out, index_any, index_ack))
  }


  # Identify potentially missed signals
  i <- which(article %in% c(article_ls$ack, article_ls$footnotes))

  if (!!length(i)) {

    index_ack$fund_ack <- get_fund_acknow(article_processed[i])
    index_ack$fund_ack_new <- get_fund_acknow_new(article_processed[i])
    index_ack$project_ack <- get_project_acknow(article_processed[i])

    index <- i[unlist(index_ack) %>% unique() %>% sort()]
    if (!!length(index)) {
      is_absent <- negate_absence_1(article_processed[index])
      index <- index[!is_absent]
    }
    index_ack %<>% purrr::map(function(x) !!length(x))

  }


  out$is_fund_pred <- !!length(index)
  out$fund_text <- article[index] %>% paste(collapse = " ")
  if (out$is_fund_pred &&
      negate_absence_1(out$fund_text) &&
      !has_positive_fund_text(out$fund_text)) {
    out$is_fund_pred <- FALSE
    out$fund_text <- ""
  }

  index_any %<>% purrr::map(function(x) !!length(x))

  if (out$is_fund_pred) {

    out$is_explicit_fund <- FALSE

  }

  # Placed here to give a chance to the title to populate the statement field
  if (!out$is_fund_pred & isTRUE(pmc_fund_ls$is_fund_pmc_group)) {

    structured_text <- c(
      pmc_fund_ls$fund_text,
      pmc_fund_ls$fund_pmc_source,
      pmc_fund_ls$fund_pmc_institute,
      pmc_fund_ls$fund_pmc_anysource
    )
    structured_text <- unique(structured_text[nzchar(structured_text)])
    structured_text <- structured_text[!grepl("^N/?A$", structured_text, ignore.case = TRUE)]
    structured_text <- paste(structured_text, collapse = "; ")

    if (nzchar(structured_text) &&
        (!negate_absence_1(structured_text) ||
         has_positive_fund_text(structured_text))) {
      out$is_fund_pred <- TRUE
      out$fund_text <- structured_text
      out$is_explicit_fund <- FALSE
    }

    return(c(relevance_ls, out, index_any, index_ack))

  }

  if (!out$is_fund_pred & isTRUE(pmc_fund_ls$is_fund_pmc_anysource)) {

    if (!negate_absence_1(pmc_fund_ls$fund_pmc_anysource) ||
        has_positive_fund_text(pmc_fund_ls$fund_pmc_anysource)) {
      out$is_fund_pred <- TRUE
      out$fund_text <- pmc_fund_ls$fund_pmc_anysource
      out$is_explicit_fund <- FALSE
    }

  }

  return(c(relevance_ls, out, index_any, index_ack))
}



#' Identify and extract Funding statements in PMC XML files.
#'
#' Takes a PMC XML file and returns data related to the
#'     presence of a Funding statement, including whether a Funding statement
#'     exists. If a Funding statement exists, it extracts it.
#'
#' @param filename The name of the PMC XML as a string.
#' @param remove_ns TRUE if an XML namespace exists, else FALSE (default).
#' @return A dataframe of results. It returns all unique article identifiers,
#'     whether this article was deemed relevant to funding (e.g. was the word
#'     "fund" found within the text), whether a funding statement was found,
#'     whether a statement within the PMC tags dedicated to funding was found,
#'     the text identified, whether this text is explicit (i.e. whether it
#'     clearly indicated that funding was received) and whether each of the
#'     labeling functions identified the text or not. The functions are
#'     returned to add flexibility in how this package is used; for example,
#'     future definitions of Funding may differ from the one we used.
#' @examples
#' \donttest{
#' # Path to a bundled example PMC XML file.
#' filepath <- system.file(
#'   "extdata", "PMID32171256-PMC7071725.xml", package = "rtransparency"
#' )
#'
#' # Identify and extract meta-data and indicators of transparency.
#' results_table <- rt_fund_pmc(filepath, remove_ns = TRUE)
#' }
#' @export
rt_fund_pmc <- function(filename, remove_ns = FALSE) {

  # A lot of the PMC XML files are malformed
  article_xml <- tryCatch(.get_xml(filename, remove_ns), error = function(e) e)

  if (inherits(article_xml, "error")) {

    return(tibble::tibble(filename = filename, is_success = FALSE))

  }

  dict <- .create_synonyms()

  id_ls <- .get_ids(article_xml)
  id_ls$filename <- filename

  # Use the same funding-detection path as rt_all_pmc: structured
  # funding-group / funding-title detection followed by text-based detection.
  pmc_fund_ls <- .get_fund_pmc(article_xml, dict)
  article_ls <- .get_article_txt(article_xml)
  fund_out <- .rt_fund_pmc(article_ls, pmc_fund_ls)
  fund_ls <- purrr::list_modify(pmc_fund_ls, !!!fund_out)

  tibble::as_tibble(c(id_ls, fund_ls, list(is_success = TRUE)))
}