{"id":"W1999391077","doi":"10.2147/clep.s69660","title":"Bias in identification of the best treatment in a Bayesian network meta-analysis for binary outcome: a simulation study","year":2014,"lang":"en","type":"article","venue":"Clinical Epidemiology","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; McMaster University","keywords":"Rank (graph theory); Statistics; Sample size determination; Bayesian probability; Meta-analysis; Computer science; Identification (biology); Bayesian network; Outcome (game theory); Mathematics; Medicine; Biology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.1876539,0.00196326,0.005185426,0.003115359,0.001058538,0.003650752,0.00250094,0.003576582,0.003312627],"category_scores_gemma":[0.3364274,0.001058215,0.01191852,0.002849774,0.001639199,0.003959686,0.002496629,0.004306876,0.0003240229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00352277,"about_ca_system_score_gemma":0.003729331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0077332,"about_ca_topic_score_gemma":0.005550615,"domain_scores_codex":[0.8402945,0.1494181,0.003505782,0.003565457,0.002528463,0.0006877385],"domain_scores_gemma":[0.4530239,0.524796,0.009055535,0.007220412,0.005220199,0.0006838662],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01706889,0.0008190532,0.03445193,0.01147208,0.04886996,0.001160933,0.001106865,0.744852,0.001411982,0.05248005,0.004054829,0.08225144],"study_design_scores_gemma":[0.006387399,0.002506872,0.004941597,0.00383727,0.04175645,0.0005893948,0.000356578,0.8426239,0.002151086,0.08859946,0.005909942,0.0003399964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2561386,0.03387142,0.6829551,0.006751432,0.0008103515,0.006446644,0.002183434,0.0006358854,0.01020711],"genre_scores_gemma":[0.8126158,0.004015876,0.1761445,0.001797821,0.0001071234,0.003555157,0.0007070665,0.00008342776,0.0009732782],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8123461,"threshold_uncertainty_score":0.9924204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9612837799759205,"score_gpt":0.691199470085655,"score_spread":0.2700843098902656,"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."}}