{"id":"W2576106406","doi":"10.1016/j.prevetmed.2017.01.006","title":"STARD-BLCM: Standards for the Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models","year":2017,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":219,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Prince Edward Island; McGill University Health Centre","funders":"Canada Excellence Research Chairs, Government of Canada; Canada Research Chairs; McGill University","keywords":"Latent class model; Bayesian probability; Computer science; Test (biology); Checklist; Diagnostic accuracy; Class (philosophy); Artificial intelligence; Machine learning; Medical physics; Data mining; Medicine; Psychology","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":["metaresearch"],"category_scores_codex":[0.4717311,0.00563151,0.01082973,0.03762177,0.003550628,0.01942465,0.02247823,0.02056772,0.02487748],"category_scores_gemma":[0.8774673,0.007693481,0.01342464,0.03268406,0.0106817,0.009724957,0.01993868,0.02586563,0.01776537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009224367,"about_ca_system_score_gemma":0.03848838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007127723,"about_ca_topic_score_gemma":0.005525917,"domain_scores_codex":[0.3131557,0.3840084,0.2251606,0.008550707,0.0666953,0.00242936],"domain_scores_gemma":[0.05925088,0.7201771,0.04905549,0.09356641,0.07482255,0.003127517],"domain_codex":"methods","domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001984884,0.0006019105,0.005165822,0.0405639,0.004304934,0.0003954193,0.002370568,0.0123105,0.0022861,0.1524525,0.4836836,0.2938799],"study_design_scores_gemma":[0.002492695,0.0008615973,0.01049066,0.0533271,0.002955873,0.001484967,0.0009479478,0.03348014,0.01051284,0.362376,0.5199855,0.001084738],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001409373,0.009702308,0.8943738,0.01632407,0.00484914,0.009516114,0.03996821,0.01480477,0.009052243],"genre_scores_gemma":[0.01501776,0.005049769,0.9089711,0.008424545,0.001350249,0.02980403,0.02628366,0.003213288,0.001885455],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5282689,"threshold_uncertainty_score":0.6514496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5081186604439791,"score_gpt":0.523944825892012,"score_spread":0.01582616544803284,"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."}}