{"id":"W2977709247","doi":"10.1002/cam4.2026","title":"Impact on immigrant screening adherence with introduction of a population‐based colon screening program in Ontario, Canada","year":2019,"lang":"en","type":"article","venue":"Cancer Medicine","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"University of British Columbia","keywords":"Immigration; Medicine; Guideline; Logistic regression; Population; Cancer screening; Intervention (counseling); Demography; Gerontology; Family medicine; Environmental health; Cancer; Internal medicine; Nursing; Geography; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000184591,0.0002146238,0.0005377183,0.0002516447,0.00004168706,0.000005120947,0.00006748697,0.00007089126,0.0006827363],"category_scores_gemma":[0.00006404229,0.0001489183,0.0000507266,0.0006608777,0.00005404021,0.00006575737,0.00001027648,0.0004218186,5.667869e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001004655,"about_ca_system_score_gemma":0.001052783,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9898123,"about_ca_topic_score_gemma":0.9860634,"domain_scores_codex":[0.9983223,0.00004088327,0.000355425,0.0003837566,0.0005965399,0.0003010981],"domain_scores_gemma":[0.9991187,0.00006610939,0.0002228219,0.000296006,0.0001467228,0.0001495749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01393005,0.00007571175,0.9176414,0.00009782909,0.00007074008,0.00001753182,0.0002244472,0.007271776,0.001315435,0.000004508988,0.0003693427,0.05898121],"study_design_scores_gemma":[0.004934573,0.0141582,0.9721299,0.001459434,0.00007721112,0.00001867746,0.0002180619,0.004806402,0.0009897135,0.000001547001,0.001046275,0.0001599335],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961647,0.0002228678,0.0003991603,0.001585764,0.0002811766,0.0009824742,0.000005421654,0.00005348522,0.00030499],"genre_scores_gemma":[0.9982063,0.000004838414,0.0008441845,0.0002173338,0.0002879796,0.0001223812,0.0001173239,0.00002360118,0.0001760584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05882128,"threshold_uncertainty_score":0.7475484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809884153261996,"score_gpt":0.292558239850812,"score_spread":0.274459398318192,"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."}}