{"id":"W6920970109","doi":"10.6084/m9.figshare.26708882.v1","title":"Additional file 1 of Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines","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 British Columbia","funders":"","keywords":"Causal inference; Inference; Group method of data handling; Key (lock); Data collection","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0268381,0.001885712,0.00247369,0.006632192,0.00108046,0.003715213,0.003552762,0.003175165,0.8585089],"category_scores_gemma":[0.3615072,0.002151573,0.003280154,0.008605693,0.0007166086,0.00346702,0.002604678,0.002322559,0.1017784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00208119,"about_ca_system_score_gemma":0.006168162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003670342,"about_ca_topic_score_gemma":0.006128924,"domain_scores_codex":[0.9870832,0.006423623,0.003416234,0.001350728,0.001349721,0.00037663],"domain_scores_gemma":[0.4434962,0.5198804,0.01101474,0.01236399,0.0118726,0.001372138],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009523489,0.0001170499,0.001816386,0.03261607,0.0005891533,0.00009915951,0.0001669885,0.001243146,0.00009303375,0.005001195,0.9331046,0.02420097],"study_design_scores_gemma":[0.02715229,0.0006359886,0.01565842,0.03105471,0.003035096,0.001039552,0.0004489822,0.008495565,0.001478767,0.0853379,0.8251947,0.0004680959],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002331607,0.0001401254,0.007317078,0.0006409343,0.0001621148,0.001510754,0.9869512,0.001382425,0.001662159],"genre_scores_gemma":[0.02653596,0.001446692,0.1628816,0.005930405,0.0009950254,0.09759332,0.6773004,0.006706402,0.02061022],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9731619,"threshold_uncertainty_score":0.2018199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9869484077061921,"score_gpt":0.7202717988866826,"score_spread":0.2666766088195095,"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."}}