{"id":"W2049989275","doi":"10.1136/ebm.14.4.100","title":"The devil is in the details...or not? A primer on individual patient data meta-analysis","year":2009,"lang":"en","type":"article","venue":"Evidence-Based Medicine","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Toronto","funders":"","keywords":"Primer (cosmetics); Meta-analysis; Psychology; Computer science; Medicine; Internal medicine; Chemistry","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":[],"domain":null,"study_design":"not_applicable","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"theoretical_or_conceptual","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","open_science","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.2285265,0.0005437169,0.005417412,0.0008406004,0.000413317,0.000815441,0.009086364,0.0001024216,0.01385253],"category_scores_gemma":[0.1280554,0.0001397389,0.002748752,0.007343765,0.0002658209,0.0004708022,0.0002005503,0.0004585692,0.0008819925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004163756,"about_ca_system_score_gemma":0.0002729213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009284546,"about_ca_topic_score_gemma":0.0004628901,"domain_scores_codex":[0.9542601,0.01934532,0.009638035,0.001845871,0.01436467,0.0005460455],"domain_scores_gemma":[0.9282451,0.05057969,0.004804595,0.01526463,0.0008712633,0.0002347576],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003654666,0.0003424189,0.006866116,0.00003075308,0.0377226,0.00008864261,0.004544716,0.0006095985,0.00002576325,0.001199991,0.7528675,0.1953365],"study_design_scores_gemma":[0.0008966767,0.001724668,0.106799,0.0002602978,0.3375427,0.0000155873,0.003936161,0.009623406,0.0001108239,0.002093666,0.5363574,0.000639667],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.0366866,0.06959125,0.009352457,0.8757004,0.0005031588,0.004918364,0.0001763534,0.00002392797,0.003047532],"genre_scores_gemma":[0.9246107,0.0002621165,0.0009209061,0.07200115,0.0001937766,0.0001104321,0.00003612709,0.00001000781,0.001854815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8879241,"threshold_uncertainty_score":0.9998959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9355634960450253,"score_gpt":0.5838908827533396,"score_spread":0.3516726132916858,"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."}}