{"id":"W3033227896","doi":"10.1101/2020.05.22.20110569","title":"The OncoSim-Breast cancer microsimulation model","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Sunnybrook Hospital; Statistics Canada; Canadian Partnership Against Cancer","funders":"Health Canada; Partenariat Canadien Contre Le Cancer","keywords":"Breast cancer; Cancer registry; Medicine; Population; Breast cancer screening; Cancer; Incidence (geometry); Ductal carcinoma; Demography; Oncology; Mammography; 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":[],"consensus_categories":[],"category_scores_codex":[0.000729193,0.000555714,0.0005605954,0.0005266113,0.0004724066,0.0009791823,0.001601826,0.001040717,0.01037234],"category_scores_gemma":[0.003712505,0.0003493703,0.0008686723,0.0005847516,0.0005478089,0.0006370576,0.0008236498,0.000840454,0.0007141805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002808083,"about_ca_system_score_gemma":0.003704024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1063254,"about_ca_topic_score_gemma":0.04000936,"domain_scores_codex":[0.9995717,0.0001523822,0.00001496024,0.00007428008,0.00007989825,0.0001066594],"domain_scores_gemma":[0.9984475,0.0008874271,0.0001527155,0.00008843222,0.0003113117,0.0001125194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003838357,0.0000146851,0.0007402534,0.00001882497,0.00001379083,0.00003535376,0.00001568779,0.9858786,0.0001147673,0.01069052,0.0009368318,0.001502261],"study_design_scores_gemma":[0.00002856976,0.00001742733,0.0002530013,0.000007398644,0.00001016488,0.0000118296,0.00001133855,0.9944185,0.0001052954,0.003198661,0.001931132,0.000006662244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4185902,0.001319378,0.4069746,0.005679846,0.000341841,0.000708467,0.01408852,0.001862108,0.150435],"genre_scores_gemma":[0.9397281,0.00044098,0.0311195,0.0003615146,0.00003970473,0.0007369235,0.0027081,0.0001216045,0.02474361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1063254,"threshold_uncertainty_score":0.2114131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1252954166586223,"score_gpt":0.3814696959064607,"score_spread":0.2561742792478384,"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."}}