{"id":"W4383187616","doi":"10.1158/1055-9965.epi-23-0326","title":"Cancer Incidence by Race and Immigration Status in Canada: Value of Enhanced Sociodemographic Data for Disease Surveillance","year":2023,"lang":"en","type":"letter","venue":"Cancer Epidemiology Biomarkers & Prevention","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Demography; Immigration; Incidence (geometry); Cancer; Population; Thyroid cancer; Medicine; Race (biology); Cancer registry; Census; Cancer incidence; Disease; Gerontology; Health equity; Environmental health; Public health; Geography; Pathology; Biology; Internal medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003880954,0.0003531072,0.0007696218,0.001620523,0.003396922,0.002306879,0.00150298,0.005442529,0.002425563],"category_scores_gemma":[0.0149511,0.0003370779,0.0006682195,0.002671971,0.002068323,0.001280018,0.001017599,0.006899093,0.0009967567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02854748,"about_ca_system_score_gemma":0.04029181,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9161859,"about_ca_topic_score_gemma":0.9585407,"domain_scores_codex":[0.9965658,0.0005044791,0.000344038,0.0002615115,0.001742081,0.0005821928],"domain_scores_gemma":[0.9834868,0.004077018,0.0007873394,0.0004881199,0.00852206,0.002638565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001100979,0.00005314427,0.07730118,0.0001548528,0.00004862276,0.001187959,0.0007150673,0.0002770294,0.0003726865,0.001376133,0.7899559,0.1284474],"study_design_scores_gemma":[0.0001641074,0.0001469493,0.2558038,0.001417473,0.0001663655,0.003162147,0.004722472,0.003732122,0.0005202923,0.00681799,0.7231308,0.0002155352],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.01215378,0.007165388,0.0003573852,0.9657279,0.00451908,0.00005221379,0.001020046,0.00008306788,0.00892109],"genre_scores_gemma":[0.164688,0.02357843,0.003642541,0.7645168,0.02092684,0.0001771591,0.001878207,0.0001338285,0.02045821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08381414,"threshold_uncertainty_score":0.2071275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09434234093270075,"score_gpt":0.3951754991071429,"score_spread":0.3008331581744422,"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."}}