{"id":"W3108606878","doi":"10.1158/1538-7755.disp20-po-021","title":"Abstract PO-021: A hub and spoke model to improve cancer care quality: Advancing Cancer Care Together (ACCT) for Asian American Medicaid beneficiaries in Orange County, California","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":"Institute of Population and Public Health","funders":"","keywords":"Vietnamese; Medicine; Medicaid; Population; Pacific islanders; Family medicine; Cancer; Community health; Health care; Health equity; Chinese americans; Gerontology; Nursing; Immigration; Environmental health; Public health; Economic growth; Political science; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.002466162,0.0003398857,0.0001937231,0.000503733,0.004257842,0.003090741,0.002100023,0.001044294,0.01745381],"category_scores_gemma":[0.003858009,0.0002322074,0.0005644912,0.0006159193,0.001008218,0.002367983,0.00619565,0.002571885,0.001407891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005950623,"about_ca_system_score_gemma":0.02940061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1072876,"about_ca_topic_score_gemma":0.2739308,"domain_scores_codex":[0.9988763,0.0003478546,0.00003239763,0.0001519428,0.000216775,0.0003748499],"domain_scores_gemma":[0.9960609,0.0001808342,0.0001562353,0.0001723243,0.000438692,0.002990961],"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":[0.0003729154,0.003822618,0.1012432,0.0003027246,0.0000996248,0.0003901839,0.005500109,0.003050986,0.0008678891,0.01829928,0.5154788,0.3505717],"study_design_scores_gemma":[0.001440027,0.003872346,0.2465754,0.001735334,0.0003885238,0.0005553671,0.04407763,0.02842451,0.001364261,0.01972767,0.6516563,0.0001825694],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5443925,0.001626556,0.01338438,0.2229625,0.001506902,0.002239718,0.00353874,0.001966994,0.2083818],"genre_scores_gemma":[0.9107023,0.001850188,0.02785091,0.01947888,0.0005142566,0.001831346,0.002648826,0.0002157928,0.03490739],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1072876,"threshold_uncertainty_score":0.2133263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1243988789957498,"score_gpt":0.4472523879187146,"score_spread":0.3228535089229648,"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."}}