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AIscreenR 0.5.0

New features

• Adding solve_or_guess() and rank_one_diagnostic() to evaluate screening performance without requiring a gold-standard label set. solve_or_guess() estimates each rater’s (system or human) solving probability from patterns of agreement alone, and rank_one_diagnostic() checks whether that model’s core assumption holds for a given fit.

Bug fixes

• read_ris_to_dataframe() now repairs invalid UTF-8 characters to avoid data loss.

AIscreenR 0.4.0

New features

• Adding tabscreen_mistral() and get_api_key_mistral() functions to screen titles and abstracts using Mistral’s API models.
• Adding tabscreen_gemini() and get_api_key_gemini() function to screen titles and abstracts using Gemini’s API models.
• Adding tabscreen_claude() and get_api_key_anthropic() function to screen titles and abstracts using Anthropics’s API models.

Major improvements

• Migrating from chat/completions endpoint to responses for all OpenAI functions. This includes:
  • tabscreen_gpt()
  • screen_errors.gpt()
  • rate_limits_per_minute()

Minor improvements

• Updated the prize function with up-to-date prizes for the GPT-5 models.
• Adding the n_screened and n_missing variables to the key results in screen_analyzer().

AIscreenR 0.3.2

Minor improvements

• Updating the default inclusion threshold and documentation hereof when conducting replicate screenings to be aligned with the finding from Vembye et al. (2025).
• Updating the handling of coding missing abstracts in the vignette now when using read_ris_to_dataframe().
• Better error messages for unknown GPT models when using newer or fine-tuned models.

Bug fixes

• Fixed bug in report() when rendering large amounts of text.
• Fixed bug in tabscreen_gpt() when using multiple reps and gpt-5 models.
• Fixed bug in screen_analyzer() when working with multiple prompts, models, and reps.
• Correcting path in generating-disagreement-reports article.
• Set max_tries in rate_limits_per_minute() to avoid message from httr2.

Further documentation

• Add installation guide to ollama article.
• Include an example of fine-tuning a model and using it to fine-tuning article.

AIscreenR 0.3.1

• Updating documentation of tabscreen_gpt()

AIscreenR 0.3.0

New features

• Adding tabscreen_groq() function to screen titles and abstracts using Groq AI.
• Adding tabscreen_ollama() function to screen titles and abstracts using local ollama models.
• Adding functions to read and write RIS files: read_ris_to_dataframe() and save_dataframe_to_ris().
• Adding function to generate disagreement reports: generate_disagreement_report().
• Making new refinements to the tabscreen_* functions. Making it possible to steer the model’s (over)inclusion behavior via the overinclusive = TRUE argument in tabscreen_* functions.

Further documentation

• Adding articles for fine-tuning OpenAI models, generating disagreement reports, generating fine-tuning data and reading/writing RIS files.
• Adding article for comparing performance of reasoning models (including gpt-5 models) with gpt-4o-mini.

Minor improvements

• Updated prize data, including all up-to-date models

AIscreenR 0.2.0

• Adding Thomas Olsen as co-author.

New features

• Adding create_fine_tune_data() and write_fine_tune_data() to generate data for fine tuning OpenAI’s models.

Minor improvements

• Minor change in the setup of the vignette.
• Updated prize data, including all up-to-date models.

AIscreenR 0.1.1

• A typo in the vignette has been corrected.
• The vignette now draws on functions from synthesisr instead of revtools to handle RIS files.
• tabscreen_gpt() now treats the study ID variable as a factor to keep original order of the dataset with titles and abstracts.

AIscreenR 0.1.0

• This is the first release of AIscreenR.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.