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.