read_ris_to_dataframe().report() when rendering large amounts of
text.tabscreen_gpt() when using multiple reps
and gpt-5 models.screen_analyzer() when working with
multiple prompts, models, and reps.rate_limits_per_minute() to avoid
message from httr2.tabscreen_gpt()tabscreen_groq() function to screen titles and
abstracts using Groq AI.tabscreen_ollama() function to screen titles and
abstracts using local ollama models.read_ris_to_dataframe() and
save_dataframe_to_ris().generate_disagreement_report().overinclusive = TRUE argument in tabscreen_*
functions.create_fine_tune_data() and
write_fine_tune_data() to generate data for fine tuning
OpenAI’s models.tabscreen_gpt() now treats the study ID variable as a
factor to keep original order of the dataset with titles and
abstracts.
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