{"id":"W3123169757","doi":"10.1158/1538-7755.modpop19-a10","title":"Abstract A10: Using large linked databases to understand cancer care access in northwestern Ontario, Canada","year":2020,"lang":"en","type":"article","venue":"Cancer Epidemiology Biomarkers & Prevention","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thunder Bay Regional Health Sciences Centre; Health Sciences North; NOSM University","funders":"","keywords":"Medicine; Residence; Odds ratio; Receipt; Cancer registry; Family medicine; Kilometer; Health care; Confidence interval; Cancer; Population; Database; Gerontology; Environmental health; Demography; Economic growth","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":[0.002882779,0.0003320799,0.0004545957,0.005297496,0.001875263,0.002620511,0.001254373,0.0004549144,0.002488315],"category_scores_gemma":[0.01657734,0.000352033,0.0007634072,0.0148546,0.0004573238,0.0007899141,0.001779013,0.0005029485,0.0003192468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02977383,"about_ca_system_score_gemma":0.03803061,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9908605,"about_ca_topic_score_gemma":0.9922274,"domain_scores_codex":[0.9969009,0.0004841898,0.0004305385,0.0006248556,0.001069173,0.0004902721],"domain_scores_gemma":[0.983889,0.003086193,0.003972733,0.001027302,0.006626427,0.001398374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008709586,0.00003384126,0.9814555,0.000158335,0.0002001378,0.00007917338,0.0005382331,0.0006323567,0.00007640812,0.0003715677,0.009402256,0.006965131],"study_design_scores_gemma":[0.00005360994,0.00002177967,0.9829172,0.0002707742,0.0001752495,0.00007340341,0.001708908,0.004756636,0.0001236455,0.0002661594,0.009600966,0.00003161066],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6877739,0.002061268,0.002295175,0.002522658,0.00004280156,0.0006462963,0.2964253,0.0001844101,0.008048129],"genre_scores_gemma":[0.9030786,0.001055233,0.004705371,0.0004467696,0.00004035713,0.0004770833,0.08889249,0.00003222249,0.001271925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02977383,"threshold_uncertainty_score":0.2160252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3301283257509696,"score_gpt":0.4585989608661729,"score_spread":0.1284706351152033,"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."}}