{"id":"W1547005602","doi":"10.1111/insr.12088","title":"A Conditional Approach to Measure Mortality Reductions Due to Cancer Screening","year":2015,"lang":"en","type":"article","venue":"International Statistical Review","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Cancer Care Ontario; Public Health Ontario; University of Toronto","funders":"National Cancer Institute; Canadian Institutes of Health Research","keywords":"Medicine; Measure (data warehouse); Statistics; Cancer screening; Econometrics; Cancer; Intensive care medicine; Computer science; Mathematics; Internal medicine; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004116558,0.0001224823,0.0002940449,0.00008130219,0.00004920585,0.00002649983,0.0001029047,0.00003671292,0.0004830765],"category_scores_gemma":[0.002021976,0.0001086859,0.00006928144,0.0003008316,0.0000350339,0.00006095624,0.00005099599,0.0001747951,0.0001382919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002932213,"about_ca_system_score_gemma":0.0001950563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005119324,"about_ca_topic_score_gemma":0.00005052773,"domain_scores_codex":[0.9983489,0.00005408284,0.0003093026,0.0003233417,0.0008016672,0.0001627324],"domain_scores_gemma":[0.998541,0.0000614752,0.00004976456,0.0001447422,0.0006552357,0.0005477555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002300407,0.0005909161,0.002607523,0.0008609333,0.0007792289,0.0001117469,0.0002312171,0.00105059,0.0001534897,0.04806494,0.5225046,0.4207444],"study_design_scores_gemma":[0.001737616,0.001420728,0.1508123,0.005462096,0.0006933297,0.001132394,0.0001020979,0.004152325,0.0001412981,0.003642291,0.830039,0.0006644824],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001853837,0.004102419,0.9546157,0.01287787,0.0006135383,0.0009143443,0.0005742888,0.00009947101,0.02434851],"genre_scores_gemma":[0.8571023,0.000627258,0.1213926,0.01552772,0.001538454,0.00146541,0.000754457,0.0000435595,0.001548225],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8552485,"threshold_uncertainty_score":0.5289349,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1687012141053966,"score_gpt":0.4258230739627404,"score_spread":0.2571218598573439,"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."}}