{"id":"W2110970000","doi":"10.1093/ije/dyn179","title":"Can trial sequential monitoring boundaries reduce spurious inferences from meta-analyses?","year":2008,"lang":"en","type":"article","venue":"International Journal of Epidemiology","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":865,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Joseph’s Healthcare Hamilton; McMaster University","funders":"","keywords":"Spurious relationship; Meta-analysis; Medicine; Inference; MEDLINE; Econometrics; Statistics; Computer science; Internal medicine; Mathematics; Artificial intelligence; Biology","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"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.5781268,0.003516193,0.006945492,0.005444744,0.00279738,0.005681792,0.008717893,0.008818762,0.003683311],"category_scores_gemma":[0.8762464,0.00295457,0.01291634,0.006833695,0.006818253,0.01238813,0.005475132,0.007882746,0.0007339357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003524589,"about_ca_system_score_gemma":0.008768952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002202805,"about_ca_topic_score_gemma":0.002465322,"domain_scores_codex":[0.3458661,0.5639105,0.04255354,0.02168614,0.02429035,0.001693414],"domain_scores_gemma":[0.07437123,0.8184831,0.04579352,0.04862614,0.01174114,0.0009849875],"domain_codex":"methods","domain_gemma":"methods","domain_candidate":"methods","domain_consensus":"methods","study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01605976,0.000678127,0.08002271,0.02076523,0.038717,0.001803141,0.008711177,0.06075531,0.002846747,0.08101508,0.03209157,0.6565341],"study_design_scores_gemma":[0.01006616,0.007997729,0.04846279,0.01445135,0.02999681,0.002873785,0.001026493,0.3049104,0.01449268,0.5162206,0.04846939,0.001031867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04248184,0.01849306,0.9068006,0.01490275,0.003630607,0.005837054,0.0008207488,0.002840402,0.004193005],"genre_scores_gemma":[0.3616428,0.00210108,0.6169631,0.006006444,0.001136036,0.01045502,0.0005713557,0.0005694627,0.0005546959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4218732,"threshold_uncertainty_score":0.5202447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9479572577499191,"score_gpt":0.6531325852215111,"score_spread":0.294824672528408,"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."}}