{"id":"W2025797841","doi":"10.1002/sim.2178","title":"Estimation of age‐specific sensitivity and sojourn time in breast cancer screening studies","year":2005,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Cancer Institute; National Institutes of Health","keywords":"Estimator; Breast cancer; Sensitivity (control systems); Statistics; Selection (genetic algorithm); Model selection; Computer science; Econometrics; Breast cancer screening; Cancer; Mathematics; Medicine; Machine learning; Mammography; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.08182726,0.0007824412,0.002307819,0.004775998,0.0003559223,0.001544407,0.001935112,0.002166846,0.0007851191],"category_scores_gemma":[0.2762587,0.0008842587,0.002866538,0.002544825,0.001495099,0.00251895,0.001821614,0.001358232,0.0001287239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001197581,"about_ca_system_score_gemma":0.0009624587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001652013,"about_ca_topic_score_gemma":0.001019418,"domain_scores_codex":[0.9530862,0.04196849,0.001409668,0.001592147,0.001557586,0.000385998],"domain_scores_gemma":[0.5468662,0.4138953,0.02261679,0.01298101,0.002822075,0.0008185913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001734279,0.0002393825,0.480323,0.00141353,0.009505619,0.0008379968,0.001843959,0.3415772,0.003169595,0.0482464,0.0007335244,0.1103756],"study_design_scores_gemma":[0.0002031913,0.001350596,0.2449841,0.0004615993,0.002879279,0.00151178,0.0003869213,0.6408838,0.005085601,0.09896499,0.003011049,0.0002770893],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6418931,0.00809444,0.3459348,0.0009729688,0.00009219348,0.0003423509,0.0003437662,0.0002535093,0.002072769],"genre_scores_gemma":[0.9791074,0.0009935268,0.01916661,0.0001500299,0.00005485415,0.0001064086,0.0001364117,0.00001474449,0.0002698271],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.08182726,"threshold_uncertainty_score":0.4327491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07831978169133007,"score_gpt":0.4016812690700812,"score_spread":0.3233614873787511,"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."}}