{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000279538,0.0006220855,0.001297129,0.0001102269,0.0002131724,0.0000472676,0.0001487126,0.0005342243,0.00008148744],"category_scores_gemma":[0.0004670742,0.0004586742,0.0003041608,0.0001331749,0.0001893379,0.00003883721,0.0004549426,0.001447307,0.00001175485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002830258,"about_ca_system_score_gemma":0.0003498369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003481942,"about_ca_topic_score_gemma":0.00007844893,"domain_scores_codex":[0.9967393,0.0004283077,0.0008080124,0.001285624,0.0003613558,0.0003773999],"domain_scores_gemma":[0.9976166,0.0003844883,0.0007958006,0.0005621478,0.0001609816,0.0004799136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001647051,0.0001415009,0.9688249,0.0002517635,0.000805372,0.0000803915,0.0005381698,0.02267318,0.0002091483,0.00005622168,0.0002545065,0.006000155],"study_design_scores_gemma":[0.001452241,0.0003134795,0.7862026,0.000293698,0.001481094,0.00001015282,0.00004917055,0.2051148,0.0001897752,0.004424058,0.0001958737,0.0002731069],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9826564,0.002913869,0.007127494,0.00216008,0.0006947271,0.001356455,0.002806248,0.0001818403,0.0001029067],"genre_scores_gemma":[0.9740996,0.01094306,0.01297763,0.0003750462,0.0008704854,0.0004379106,0.0002088934,0.00006802403,0.00001930618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1826223,"threshold_uncertainty_score":0.9997865,"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."}}