Example rows where human screening decisions differ from GPT decisions. Each row is a (study × prompt) screening outcome.
Format
A tibble/data.frame with one row per screened (studyid, promptid) and 17 columns:
| author | character | Study authors |
| human_code | numeric | Human screening decision (1 include, 0 exclude) |
| studyid | integer | Unique study identifier |
| title | character | Study title |
| abstract | character | Study abstract |
| promptid | integer | Prompt identifier |
| prompt | character | Original short screening prompt text |
| model | character | Model used for the run |
| question | character | Full constructed question sent to model |
| top_p | numeric | Nucleus sampling parameter |
| incl_p | numeric | Estimated probability of inclusion (if repetitions) |
| final_decision_gpt | character | GPT final label: Include / Exclude / Check |
| final_decision_gpt_num | numeric | Numeric GPT decision (1 include/check, 0 exclude) |
| longest_answer | character | Longest rationale text returned |
| reps | integer | Number of repetitions attempted |
| n_mis_answers | integer | Count of missing answers across reps |
| submodel | character | Specific model variant (if applicable) |