{"id":"W2790457387","doi":"10.1093/jcag/gwy009.198","title":"A198 PERSONALIZING THE AGE TO STOP COLORECTAL CANCER SCREENING IN CANADA BASED ON COMORBIDITY AND PRIOR SCREENING HISTORY: MODEL ESTIMATES OF HARMS AND BENEFITS","year":2018,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; Health Sciences Centre; Cancer Care Ontario; Sunnybrook Health Science Centre","funders":"","keywords":"Comorbidity; Medicine; Cohort; Microsimulation; Population; Demography; Colorectal cancer screening; Cancer screening; Colorectal cancer; Gerontology; Cancer; Internal medicine; Environmental health; Colonoscopy","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001593241,0.00116836,0.001262498,0.001067867,0.001086661,0.001572199,0.002554533,0.001688074,0.007071944],"category_scores_gemma":[0.004308862,0.0006500125,0.001482232,0.001144162,0.001051405,0.0005837259,0.00111663,0.001531397,0.000413026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01717318,"about_ca_system_score_gemma":0.02089982,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9081207,"about_ca_topic_score_gemma":0.8047272,"domain_scores_codex":[0.9993176,0.0001714569,0.0000179363,0.0001123128,0.0000706533,0.000310015],"domain_scores_gemma":[0.9977242,0.001180472,0.0002377654,0.00005450761,0.0005318589,0.0002711725],"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":[0.0002303279,0.00006865078,0.01574387,0.00005302923,0.0001561163,0.00008652803,0.00006924381,0.969928,0.000138758,0.00733464,0.002713079,0.003477625],"study_design_scores_gemma":[0.0001343037,0.00006166978,0.005956914,0.00003817256,0.0001566044,0.00003500296,0.00008911109,0.9893537,0.00006668748,0.002430071,0.001648132,0.00002962492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8712586,0.002274688,0.05283375,0.007665487,0.0001858247,0.0007825801,0.02168127,0.0004521178,0.0428658],"genre_scores_gemma":[0.9631532,0.0008873748,0.009522447,0.000488482,0.00005084471,0.0004504783,0.003504435,0.00004203846,0.02190059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09187931,"threshold_uncertainty_score":0.1848407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05353331768910933,"score_gpt":0.2778029151886168,"score_spread":0.2242695974995075,"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."}}