{"id":"W3043139790","doi":"10.1002/sim.8584","title":"A general method for elicitation, imputation, and sensitivity analysis for incomplete repeated binary data","year":2020,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"NIHR School for Primary Care Research; Medical Research Council Canada; Medical Research Council; National Institute for Health and Care Research","keywords":"Pooling; Missing data; Imputation (statistics); Expert opinion; Expert elicitation; Statistics; Computer science; Econometrics; Medicine; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.1360953,0.003364649,0.004427174,0.008036437,0.001754063,0.003657839,0.004725307,0.003582166,0.01595218],"category_scores_gemma":[0.3327419,0.003126297,0.008016099,0.006983652,0.003485761,0.004024894,0.007394305,0.008117261,0.002986757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002530319,"about_ca_system_score_gemma":0.006834042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002695478,"about_ca_topic_score_gemma":0.002579381,"domain_scores_codex":[0.8196725,0.1596469,0.005511345,0.004502681,0.009835472,0.0008311364],"domain_scores_gemma":[0.6624086,0.2942905,0.009775685,0.02480423,0.008117535,0.0006034103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004831387,0.000282607,0.003362135,0.003834757,0.002830016,0.0006203878,0.001885942,0.1372961,0.002734516,0.5262716,0.01787045,0.3025283],"study_design_scores_gemma":[0.0003697047,0.0003070703,0.001098001,0.001001936,0.0005254502,0.000596709,0.0001809544,0.3371067,0.002902771,0.6162328,0.03935903,0.0003188683],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001644138,0.00005503445,0.9983222,0.0001024598,0.00002314012,0.000625804,0.0001680321,0.0002210708,0.0003179369],"genre_scores_gemma":[0.009281385,0.0001908229,0.9824572,0.0001901285,0.00006042522,0.006912858,0.0002779863,0.0001840563,0.0004450147],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1360953,"threshold_uncertainty_score":0.7197492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1664755843453659,"score_gpt":0.4701183694083897,"score_spread":0.3036427850630238,"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."}}