{"id":"W4318973081","doi":"10.1136/bmjebm-2022-112053","title":"Different meta-analysis methods can change judgements about imprecision of effect estimates: a meta-epidemiological study","year":2023,"lang":"en","type":"review","venue":"BMJ evidence-based medicine","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Impact","funders":"","keywords":"Meta-analysis; Statistics; Random effects model; Mathematics; Medicine; Restricted maximum likelihood; Maximum likelihood; Econometrics; Internal medicine","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","metaepi_broad","metaepi_narrow"],"domain":"methods","study_design":"systematic_review","genre":"review","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"},{"model":"gpt","categories":["metaresearch","metaepi_narrow","metaepi_broad"],"domain":"methods","study_design":"meta_analysis","genre":"review","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","metaepi_broad"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5248833,0.004794418,0.01005489,0.0132124,0.001783651,0.01335454,0.005379064,0.0102246,0.002426537],"category_scores_gemma":[0.8112393,0.003440132,0.0466065,0.01035938,0.004424629,0.009911896,0.0052897,0.00863645,0.0003976717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005037719,"about_ca_system_score_gemma":0.003658023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00163268,"about_ca_topic_score_gemma":0.002557819,"domain_scores_codex":[0.2361173,0.6676142,0.05599638,0.009699129,0.02970578,0.0008672691],"domain_scores_gemma":[0.07915136,0.8597425,0.03195613,0.01903187,0.009654375,0.0004637458],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"meta_analysis","study_design_gemma":"meta_analysis","study_design_scores_codex":[0.01840215,0.000358173,0.04309938,0.09420867,0.6720253,0.0007960626,0.003513871,0.01104558,0.001211441,0.009483041,0.003898766,0.1419576],"study_design_scores_gemma":[0.01632221,0.005534968,0.03397552,0.09629603,0.7514195,0.001906722,0.001710857,0.02348741,0.004807226,0.05042958,0.01279096,0.001318978],"study_design_candidate":"meta_analysis","study_design_consensus":"meta_analysis","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.07259309,0.7599143,0.135638,0.01404818,0.004762994,0.006024541,0.001307119,0.000470679,0.00524109],"genre_scores_gemma":[0.6757234,0.09723771,0.2056617,0.008544596,0.001803012,0.009344206,0.0007447173,0.0003418459,0.0005988555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9899451,"threshold_uncertainty_score":0.5859035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9820049695252123,"score_gpt":0.7362051535903333,"score_spread":0.245799815934879,"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."}}