{"id":"W3206078941","doi":"10.1016/j.chest.2021.07.1465","title":"CHARACTERIZING REGIONAL VARIABILITY IN LUNG CANCER OUTCOMES ACROSS ONTARIO: A POPULATION-BASED ANALYSIS","year":2021,"lang":"en","type":"article","venue":"CHEST Journal","topic":"Global Cancer Incidence and Screening","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Medicine; Rurality; Cancer registry; Incidence (geometry); Lung cancer; Population; Retrospective cohort study; Demography; Rural area; Cancer; Disease; Cohort; Internal medicine; Environmental health; Pathology","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":[0.001173242,0.0003539979,0.0005599491,0.001356381,0.001729408,0.001103904,0.001243059,0.000530642,0.00117823],"category_scores_gemma":[0.003609137,0.0003761967,0.001339964,0.004583803,0.0007543874,0.0004747065,0.001118388,0.0004701367,0.0001634071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01337182,"about_ca_system_score_gemma":0.01405969,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9694824,"about_ca_topic_score_gemma":0.9841924,"domain_scores_codex":[0.9986789,0.0002023861,0.0001002304,0.0003153745,0.0003482806,0.0003548398],"domain_scores_gemma":[0.9973895,0.0003505409,0.0007648172,0.0002643178,0.00086084,0.0003699617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007721366,0.00001486224,0.9967084,0.0000186352,0.0001826458,0.00005594205,0.0005967593,0.0001869826,0.0001845782,0.0000486063,0.0004864459,0.001438842],"study_design_scores_gemma":[0.000004220091,0.00001408051,0.998791,0.000005248268,0.00005654745,0.00002641101,0.0004757123,0.0002657014,0.00001932931,0.00001531597,0.0003215054,0.000004952129],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949008,0.0001937058,0.0002924354,0.0001376746,0.000004345576,0.0000310699,0.00377875,0.00001080841,0.0006503165],"genre_scores_gemma":[0.9975689,0.0001035259,0.0002110057,0.00004410448,0.000004383391,0.0000202515,0.001683217,0.000006667976,0.000357855],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03051764,"threshold_uncertainty_score":0.09701979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07322612256149699,"score_gpt":0.3860565791810951,"score_spread":0.3128304566195981,"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."}}