{"id":"W3111826093","doi":"10.1101/2020.12.04.20244046","title":"A Comprehensive Epithelial Tubo-Ovarian Cancer Risk Prediction Model Incorporating Genetic and Epidemiological Risk Factors","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Ovarian cancer diagnosis and treatment","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; European Commission; University College London; Cancer Research UK; Government of Canada; Fondation du cancer du sein du Québec; Canadian Institutes of Health Research; National Institute for Health and Care Research; Genome Canada","keywords":"Epidemiology; Medicine; Oncology; Ovarian cancer; Percentile; Relative risk; Risk assessment; Epithelial ovarian cancer; Population; Internal medicine; Genetic epidemiology; Polygenic risk score; Genetic model; Demography; Gynecology; Cancer; Environmental health; Biology; Statistics; Genetics; Confidence interval; Genotype; Gene; Computer science; Single-nucleotide polymorphism","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001763911,0.0007985656,0.0009368029,0.0007058945,0.0003615737,0.0008949779,0.001068266,0.0008258208,0.002619867],"category_scores_gemma":[0.002694893,0.0003922982,0.001256662,0.0005587107,0.0003106899,0.0004280791,0.0006937056,0.0007320438,0.0003587322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007885537,"about_ca_system_score_gemma":0.001358189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.026874,"about_ca_topic_score_gemma":0.01208718,"domain_scores_codex":[0.9995054,0.0002163071,0.00002010518,0.0001475281,0.00004435641,0.00006632536],"domain_scores_gemma":[0.9985752,0.0009350024,0.0001255809,0.00005708068,0.0002195513,0.00008756682],"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":[0.0001285839,0.00006441641,0.01289214,0.00002697105,0.0001602625,0.0001238892,0.00002890633,0.9768859,0.0002668107,0.0009406501,0.0004287064,0.008052889],"study_design_scores_gemma":[0.00001050304,0.00002883325,0.001780865,0.000005287407,0.0000383883,0.00002333821,0.000005605243,0.9974667,0.00003070399,0.0004949271,0.0001092899,0.000005476505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8204964,0.0009740019,0.1720146,0.0008612544,0.00007391663,0.0001177627,0.002532887,0.000479101,0.002450019],"genre_scores_gemma":[0.9836601,0.000222497,0.01172511,0.00008466192,0.00003472345,0.0001104978,0.001501873,0.00002023449,0.002640406],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.026874,"threshold_uncertainty_score":0.05343515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06537153310662838,"score_gpt":0.3083089333775914,"score_spread":0.242937400270963,"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."}}