{"id":"W7097511534","doi":"","title":"Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests. Biometrics","year":2001,"lang":"en","type":"article","venue":"","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; A priori and a posteriori; Inference; Conditional dependence; Bayesian inference; Conditional independence; Statistical hypothesis testing; Posterior probability; Identification (biology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0435284,0.00243314,0.003248731,0.006252549,0.001815039,0.003139802,0.005439838,0.004014264,0.006419767],"category_scores_gemma":[0.1316111,0.002866043,0.003225155,0.005355846,0.00560478,0.004493245,0.00364201,0.006330241,0.001121879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00493032,"about_ca_system_score_gemma":0.003364901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02713679,"about_ca_topic_score_gemma":0.02667646,"domain_scores_codex":[0.9698272,0.0237796,0.0008407442,0.002446834,0.002609027,0.0004965559],"domain_scores_gemma":[0.8839069,0.1028671,0.006229348,0.003231244,0.002990017,0.0007754267],"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":[0.0001736722,0.0001375789,0.008276914,0.0006405842,0.001038376,0.0004884437,0.0009658415,0.2606432,0.0004029966,0.5772416,0.006285768,0.1437051],"study_design_scores_gemma":[0.00004839837,0.00004807608,0.001957323,0.0001533887,0.0001559603,0.0002196851,0.00007691244,0.3559831,0.0001301811,0.6372279,0.003931602,0.00006749982],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002849201,0.00240836,0.9913961,0.001501658,0.00009273792,0.0001140276,0.0003144457,0.0001694246,0.001153935],"genre_scores_gemma":[0.211153,0.006115392,0.7730954,0.0009645409,0.0007770424,0.001590952,0.001035444,0.0001742508,0.005093974],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0435284,"threshold_uncertainty_score":0.2302029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3592457006525436,"score_gpt":0.3780942272679756,"score_spread":0.01884852661543202,"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."}}