{"id":"W3134172389","doi":"10.5888/pcd18.200362","title":"Spatial Insights for Understanding Colorectal Cancer Screening in Disproportionately Affected Populations, Central Texas, 2019","year":2021,"lang":"en","type":"article","venue":"Preventing Chronic Disease","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Esri (Canada)","funders":"Dell Medical School, University of Texas at Austin; Cancer Prevention and Research Institute of Texas; Texas Department of State Health Services; U.S. Department of State","keywords":"Medicine; Socioeconomic status; Psychological intervention; Logistic regression; Odds ratio; Odds; Health care; Cluster (spacecraft); Demography; Health equity; Medical record; Cancer screening; Gerontology; Family medicine; Environmental health; Public health; Population; Cancer; Internal medicine; Nursing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001209006,0.0001678933,0.0002467942,0.0001317265,0.0001618009,0.00004841568,0.00004943466,0.00007117344,0.000144599],"category_scores_gemma":[0.0002823534,0.0001754421,0.0001808547,0.000361254,0.00003575063,0.0001467611,0.00005227057,0.0001825463,0.000002187309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009964908,"about_ca_system_score_gemma":0.001104496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007437331,"about_ca_topic_score_gemma":0.006340841,"domain_scores_codex":[0.9984892,0.00007170162,0.0003281354,0.0004277404,0.0002674483,0.0004157913],"domain_scores_gemma":[0.9993453,0.00005475911,0.0001346872,0.0001418724,0.0000904155,0.0002329556],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00738691,0.0008041005,0.9120341,0.001503745,0.0002996614,0.0002694924,0.0004338107,0.004614196,0.003780523,0.001882191,0.0006447737,0.06634647],"study_design_scores_gemma":[0.003375082,0.0002417913,0.9399622,0.0009445771,0.000211827,0.000009986924,0.00008756643,0.05156159,0.001588026,0.001435258,0.0003321268,0.0002499342],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9276829,0.005283225,0.0646373,0.0005344653,0.0006405338,0.0009166386,0.00004502651,0.0001361948,0.0001236728],"genre_scores_gemma":[0.9978707,0.00006356355,0.0006639589,0.00003910983,0.0004716123,0.0001159945,0.0004382325,0.00003334326,0.0003035126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07018773,"threshold_uncertainty_score":0.7154322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04643538041491317,"score_gpt":0.3244591961344284,"score_spread":0.2780238157195152,"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."}}