tabscreen_gpt.original: Title and abstract screening with GPT API models using...

In AIscreenR: AI Screening Tools in R for Systematic Reviewing

Title and abstract screening with GPT API models using function calls via the original function call arguments

Description

This function has been deprecated (but can still be used) because OpenAI has deprecated the function_call and and functions argument which is used in this function. Instead use the tabscreen_gpt.tools() that handles the function calling via the tools and tool_choice arguments.

This function supports the conduct of title and abstract screening with GPT API models in R. This function only works with GPT-4, more specifically gpt-4-0613. To draw on other models, use tabscreen_gpt.tools(). The function allows to run title and abstract screening across multiple prompts and with repeated questions to check for consistency across answers. This function draws on the newly developed function calling to better steer the output of the responses. This function was used in Vembye et al. (2024).

Usage

tabscreen_gpt.original(
  data,
  prompt,
  studyid,
  title,
  abstract,
  ...,
  model = "gpt-4",
  role = "user",
  functions = incl_function_simple,
  function_call_name = list(name = "inclusion_decision_simple"),
  top_p = 1,
  time_info = TRUE,
  token_info = TRUE,
  api_key = get_api_key(),
  max_tries = 16,
  max_seconds = NULL,
  is_transient = gpt_is_transient,
  backoff = NULL,
  after = NULL,
  rpm = 10000,
  reps = 1,
  seed_par = NULL,
  progress = TRUE,
  messages = TRUE,
  incl_cutoff_upper = 0.5,
  incl_cutoff_lower = incl_cutoff_upper - 0.1,
  force = FALSE
)

Arguments

data	Dataset containing the titles and abstracts.
prompt	Prompt(s) to be added before the title and abstract.
studyid	Unique Study ID. If missing, this is generated automatically.
title	Name of the variable containing the title information.
abstract	Name of variable containing the abstract information.
...	Further argument to pass to the request body. See https://platform.openai.com/docs/api-reference/chat/create.
model	Character string with the name of the completion model. Can take multiple models, including gpt-4 models. Default = "gpt-4" (i.e., gpt-4-0613). This model has been shown to outperform the gpt-3.5-turbo models in terms of its ability to detect relevant studies (Vembye et al., Under preparation). Find available model at https://platform.openai.com/docs/models/model-endpoint-compatibility.
role	Character string indicate the role of the user. Default is "user".
functions	Function to steer output. Default is incl_function_simple. To get detailed responses use the hidden function call incl_function from the package. Also see 'Examples below. Find further documentation for function calling at https://openai.com/blog/function-calling-and-other-api-updates.
function_call_name	Functions to call. Default is list(name = "inclusion_decision_simple"). To get detailed responses use list(name = "inclusion_decision"). Also see 'Examples below.
top_p	'An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend altering this or temperature but not both.' (OPEN-AI). Default is 1. Find documentation at https://platform.openai.com/docs/api-reference/chat/create#chat/create-top_p.
time_info	Logical indicating whether the run time of each request/question should be included in the data. Default = TRUE.
token_info	Logical indicating whether the number of prompt and completion tokens per request should be included in the output data. Default = TRUE. When TRUE, the output object will include price information of the conducted screening.
api_key	Numerical value with your personal API key. Find at https://platform.openai.com/account/api-keys. Use httr2::secret_make_key(), httr2::secret_encrypt(), and httr2::secret_decrypt() to scramble and decrypt the api key and use set_api_key() to securely automate the use of the api key by setting the api key as a locale environment variable.
max_tries, max_seconds	'Cap the maximum number of attempts with max_tries or the total elapsed time from the first request with max_seconds. If neither option is supplied (the default), httr2::req_perform() will not retry' (Wickham, 2023).
is_transient	'A predicate function that takes a single argument (the response) and returns TRUE or FALSE specifying whether or not the response represents a transient error' (Wickham, 2023).
backoff	'A function that takes a single argument (the number of failed attempts so far) and returns the number of seconds to wait' (Wickham, 2023).
after	'A function that takes a single argument (the response) and returns either a number of seconds to wait or NULL, which indicates that a precise wait time is not available that the backoff strategy should be used instead' (Wickham, 2023).
rpm	Numerical value indicating the number of requests per minute (rpm) available for the specified api key. Find more information at https://platform.openai.com/docs/guides/rate-limits/what-are-the-rate-limits-for-our-api. Alternatively, use rate_limits_per_minute().
reps	Numerical value indicating the number of times the same question should be sent to OpenAI's GPT API models. This can be useful to test consistency between answers. Default is 1 but when using 3.5 models, we recommend setting this value to 10.
seed_par	Numerical value for a seed to ensure that proper, parallel-safe random numbers are produced.
progress	Logical indicating whether a progress line should be shown when running the title and abstract screening in parallel. Default is TRUE.
messages	Logical indicating whether to print messages embedded in the function. Default is TRUE.
incl_cutoff_upper	Numerical value indicating the probability threshold for which a studies should be included. Default is 0.5, which indicates that titles and abstracts that OpenAI's GPT API model has included more than 50 percent of the times should be included.
incl_cutoff_lower	Numerical value indicating the probability threshold above which studies should be check by a human. Default is 0.4, which means that if you ask OpenAI's GPT API model the same questions 10 times and it includes the title and abstract 4 times, we suggest that the study should be check by a human.
force	Logical argument indicating whether to force the function to use more than 10 iterations for gpt-3.5 models and more than 1 iteration for gpt-4 models. This argument is developed to avoid the conduct of wrong and extreme sized screening. Default is FALSE.

Value

An object of class "chatgpt". The object is a list containing the following components:

answer_data_sum	dataset with the summarized, probabilistic inclusion decision for each title and abstract across multiple repeated questions.
answer_data_all	dataset with all individual answers.
price	numerical value indicating the total price (in USD) of the screening.
price_data	dataset with prices across all gpt models used for screening.

Note

The answer_data_sum data contains the following mandatory variables:

studyid	integer	indicating the study ID of the reference.
title	character	indicating the title of the reference.
abstract	character	indicating the abstract of the reference.
promptid	integer	indicating the prompt ID.
prompt	character	indicating the prompt.
model	character	indicating the specific gpt-model used.
question	character	indicating the final question sent to OpenAI's GPT API models.
top_p	numeric	indicating the applied top_p.
incl_p	numeric	indicating the probability of inclusion calculated across multiple repeated responses on the same title and abstract.
final_decision_gpt	character	indicating the final decision reached by gpt - either 'Include', 'Exclude', or 'Check'.
final_decision_gpt_num	integer	indicating the final numeric decision reached by gpt - either 1 or 0.
longest_answer	character	indicating the longest gpt response obtained across multiple repeated responses on the same title and abstract. Only included if the detailed function calling function is used. See 'Examples' below for how to use this function.
reps	integer	indicating the number of times the same question has been sent to OpenAI's GPT API models.
n_mis_answers	integer	indicating the number of missing responses.

The answer_data_all data contains the following mandatory variables:

studyid	integer	indicating the study ID of the reference.
title	character	indicating the title of the reference.
abstract	character	indicating the abstract of the reference.
promptid	integer	indicating the prompt ID.
prompt	character	indicating the prompt.
model	character	indicating the specific gpt-model used.
iterations	numeric	indicating the number of times the same question has been sent to OpenAI's GPT API models.
question	character	indicating the final question sent to OpenAI's GPT API models.
top_p	numeric	indicating the applied top_p.
decision_gpt	character	indicating the raw gpt decision - either "1", "0", "1.1" for inclusion, exclusion, or uncertainty, respectively.
detailed_description	character	indicating detailed description of the given decision made by OpenAI's GPT API models. Only included if the detailed function calling function is used. See 'Examples' below for how to use this function.
decision_binary	integer	indicating the binary gpt decision, that is 1 for inclusion and 0 for exclusion. 1.1 decision are coded equal to 1 in this case.
prompt_tokens	integer	indicating the number of prompt tokens sent to the server for the given request.
completion_tokens	integer	indicating the number of completion tokens sent to the server for the given request.
run_time	numeric	indicating the time it took to obtain a response from the server for the given request.
n	integer	indicating request ID.

If any requests failed to reach the server, the chatgpt object contains an error data set (error_data) having the same variables as answer_data_all but with failed request references only.

The price_data data contains the following variables:

model	character	gpt model.
input_price_dollar	integer	price for all prompt/input tokens for the correspondent gpt-model.
output_price_dollar	integer	price for all completion/output tokens for the correspondent gpt-model.
price_total_dollar	integer	total price for all tokens for the correspondent gpt-model.

Find current token pricing at https://openai.com/pricing.

References

Vembye, M. H., Christensen, J., Mølgaard, A. B., & Schytt, F. L. W. (2024) GPT API Models Can Function as Highly Reliable Second Screeners of Titles and Abstracts in Systematic Reviews: A Proof of Concept and Common Guidelines https://osf.io/preprints/osf/yrhzm

Wickham H (2023). httr2: Perform HTTP Requests and Process the Responses. https://httr2.r-lib.org, https://github.com/r-lib/httr2.

Examples

## Not run: 

set_api_key()

prompt <- "Is this study about a Functional Family Therapy (FFT) intervention?"

tabscreen_gpt.original(
  data = filges2015_dat[1:2,],
  prompt = prompt,
  studyid = studyid,
  title = title,
  abstract = abstract,
  max_tries = 2
  )

 # Get detailed descriptions of the gpt decisions by using the
 # embedded function calling functions from the package. See example below.
 tabscreen_gpt.original(
   data = filges2015_dat[1:2,],
   prompt = prompt,
   studyid = studyid,
   title = title,
   abstract = abstract,
   functions = incl_function,
   function_call_name = list(name = "inclusion_decision"),
   max_tries = 2
 )

## End(Not run)

AIscreenR documentation built on April 3, 2025, 7:11 p.m.