{"id":"W2169987255","doi":"10.1002/sim.6619","title":"Inference on cancer screening exam accuracy using population‐level administrative data","year":2015,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Public Health Ontario; University of Toronto; Cancer Care Ontario","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Cancer Society; Cancer Care Ontario","keywords":"Covariate; Markov chain Monte Carlo; Bayesian probability; Statistics; Breast cancer; Inference; Computer science; Population; Cancer registry; Posterior probability; Bayesian inference; Disease; Econometrics; Cancer; Medicine; Artificial intelligence; Mathematics; Internal medicine; Environmental health","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.02458669,0.0004565545,0.001507431,0.002678098,0.0005658136,0.001991028,0.001680815,0.001213614,0.0009770478],"category_scores_gemma":[0.1269147,0.001001344,0.001392445,0.00273596,0.001380678,0.001575031,0.001381827,0.001820993,0.0002951839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002576574,"about_ca_system_score_gemma":0.002617226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06467535,"about_ca_topic_score_gemma":0.05305114,"domain_scores_codex":[0.9887646,0.007571283,0.0005452564,0.001241894,0.001479237,0.0003976102],"domain_scores_gemma":[0.9073459,0.07927152,0.005771391,0.005003476,0.002145688,0.0004620117],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004149114,0.0003104344,0.5225453,0.0002101197,0.00120723,0.0003057577,0.00067789,0.3411636,0.0006943576,0.04584859,0.003408521,0.08321319],"study_design_scores_gemma":[0.0001159686,0.0000986659,0.1287954,0.0001391086,0.0003238844,0.0002201581,0.0001860864,0.8181377,0.0009410722,0.04858454,0.002388338,0.00006901904],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5139944,0.001220576,0.4750246,0.002367159,0.00004808306,0.0001560716,0.003755381,0.0005046564,0.002929121],"genre_scores_gemma":[0.9460327,0.0005824501,0.04926861,0.0002854295,0.00007137487,0.00009588378,0.002960611,0.00003267867,0.0006702668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9754133,"threshold_uncertainty_score":0.1300284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7226447359803319,"score_gpt":0.5708361636674781,"score_spread":0.1518085723128537,"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."}}