{"id":"W4281691577","doi":"10.2337/db22-265-or","title":"265-OR: Impact of the COVID-Pandemic on Diabetes Screening from 20to 2021 in Ontario, Canada","year":2022,"lang":"en","type":"article","venue":"Diabetes","topic":"Global Public Health Policies and Epidemiology","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Medicine; Prediabetes; Diabetes mellitus; Demography; Incidence (geometry); Population; Coronavirus disease 2019 (COVID-19); Health care; Gerontology; Type 2 diabetes; Environmental health; Disease; Internal medicine; Infectious disease (medical specialty)","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":[0.0007408441,0.0003562136,0.0004101481,0.00101067,0.002415378,0.00124354,0.001253695,0.00053832,0.004815354],"category_scores_gemma":[0.003013698,0.0003321084,0.0009856618,0.002924937,0.0005282187,0.0006004017,0.001356642,0.001103194,0.0004683169],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04843094,"about_ca_system_score_gemma":0.0754296,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9981041,"about_ca_topic_score_gemma":0.9988528,"domain_scores_codex":[0.9987034,0.00006989647,0.00008268801,0.0001300313,0.0003745781,0.0006394379],"domain_scores_gemma":[0.9967083,0.0001176733,0.0006432437,0.00005670986,0.001368413,0.001105655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001856952,0.00006966967,0.9674689,0.0001994453,0.00009097606,0.0002300129,0.0008712418,0.000342329,0.0001735954,0.0002538635,0.01817673,0.01193754],"study_design_scores_gemma":[0.00001371235,0.00001983921,0.9959402,0.00008882707,0.00002347263,0.00003808688,0.0008840929,0.0003289423,0.00003134453,0.00002643218,0.002591322,0.00001378827],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8806043,0.004572141,0.0003111167,0.009380515,0.0001926621,0.000278429,0.08468829,0.0001132424,0.01985927],"genre_scores_gemma":[0.977975,0.002022525,0.0003915232,0.001747115,0.00005171324,0.00008929887,0.0127793,0.00002596186,0.00491754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9515691,"threshold_uncertainty_score":0.3513927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03452030042967372,"score_gpt":0.2740535607567529,"score_spread":0.2395332603270792,"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."}}