{"id":"W4250965155","doi":"10.1158/1538-7755.carisk16-b17","title":"Abstract B17: Comprehensive colorectal cancer risk prediction to inform personalized screening and intervention","year":2017,"lang":"en","type":"article","venue":"Cancer Epidemiology Biomarkers & Prevention","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Cancer; Colorectal cancer; Citation; Intervention (counseling); Medicine; Gerontology; White (mutation); Library science; Oncology; Internal medicine; Computer science; Biology; Psychiatry; Genetics","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":[0.007012384,0.001464199,0.001248876,0.002387618,0.0007472642,0.004656208,0.0009407825,0.001812483,0.05825304],"category_scores_gemma":[0.02048582,0.0004365894,0.001169817,0.00173208,0.0005630854,0.001477763,0.002211343,0.00262347,0.01448492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001553042,"about_ca_system_score_gemma":0.005680343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004308105,"about_ca_topic_score_gemma":0.005912353,"domain_scores_codex":[0.9977442,0.001205925,0.0001175439,0.0003070988,0.0004906426,0.0001346003],"domain_scores_gemma":[0.9938936,0.002532376,0.000653444,0.0003621341,0.001526378,0.001032091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007131705,0.0001809888,0.01711469,0.001687511,0.0004108549,0.000134597,0.0001382787,0.003318077,0.001130312,0.006967433,0.5674043,0.4007998],"study_design_scores_gemma":[0.0009771719,0.001939781,0.08654635,0.008177937,0.002131716,0.0008965897,0.001076741,0.03921692,0.008939373,0.08190672,0.7677841,0.0004066355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.05461386,0.07814436,0.1344634,0.409214,0.02653696,0.002671106,0.07828832,0.008030456,0.2080376],"genre_scores_gemma":[0.5222128,0.07396341,0.1459444,0.03462736,0.02287787,0.002828945,0.05810108,0.001455871,0.1379883],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05825304,"threshold_uncertainty_score":0.1948758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05363729525855996,"score_gpt":0.4097620313023526,"score_spread":0.3561247360437926,"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."}}