{"id":"W6921129223","doi":"10.6084/m9.figshare.26648127.v1","title":"Additional file 1 of Data extraction and comparison for complex systematic reviews: a step-by-step guideline and an implementation example using open-source software","year":2024,"lang":"en","type":"article","venue":"Figshare","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Software; Data extraction; Data file; Guideline; File format; Key (lock)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch"],"domain":"methods","study_design":"not_applicable","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["metaresearch"],"domain":"methods","study_design":"not_applicable","genre":"dataset","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01348531,0.002320285,0.002958304,0.009386741,0.001113126,0.003348583,0.002531687,0.002164222,0.8466714],"category_scores_gemma":[0.1390777,0.002084348,0.003453696,0.01043502,0.0006227541,0.003732736,0.003120336,0.001807767,0.1208268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002766951,"about_ca_system_score_gemma":0.007261994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003813519,"about_ca_topic_score_gemma":0.006818877,"domain_scores_codex":[0.9942718,0.001790782,0.002253362,0.0007546982,0.0006802247,0.000249221],"domain_scores_gemma":[0.8015329,0.1747948,0.009484892,0.0042284,0.008648365,0.001310629],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001529089,0.0001760506,0.001979946,0.09159357,0.0007520757,0.0001919862,0.0003602313,0.001250858,0.0003707426,0.004882463,0.8515786,0.04533429],"study_design_scores_gemma":[0.01771393,0.0005279309,0.01150375,0.03011171,0.001886031,0.0005681925,0.0005220714,0.003288132,0.00205912,0.03410594,0.8973101,0.0004030675],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.000427214,0.0002207993,0.00552494,0.0005674976,0.0001296523,0.003633817,0.9842774,0.002884363,0.002334418],"genre_scores_gemma":[0.01363512,0.001718631,0.1675788,0.003165531,0.0006611741,0.1646955,0.6120681,0.00761497,0.02886204],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9865147,"threshold_uncertainty_score":0.2187046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9101563193833412,"score_gpt":0.6216882235221247,"score_spread":0.2884680958612165,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}