{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006396332,0.0003291803,0.0002072364,0.002723478,0.0007464848,0.001527813,0.000612992,0.0003546361,0.003155825],"category_scores_gemma":[0.00370957,0.0001861961,0.00037135,0.003338561,0.0007229497,0.001281912,0.001606109,0.0004806389,0.0002212933],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001945695,"about_ca_system_score_gemma":0.002508515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1746707,"about_ca_topic_score_gemma":0.2139662,"domain_scores_codex":[0.9997874,0.00008097465,0.00001573924,0.00004957709,0.00002667122,0.00003968824],"domain_scores_gemma":[0.9989461,0.0002792448,0.0003593999,0.00007202195,0.0002108868,0.0001323393],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003278493,0.00006176621,0.9379806,0.0002387527,0.0001066842,0.0002893435,0.005102973,0.003015677,0.00033994,0.006288948,0.01288773,0.03365489],"study_design_scores_gemma":[0.000008507322,0.0000391498,0.9363775,0.0003563801,0.00008394464,0.0003030794,0.02381528,0.008030021,0.0001174751,0.009956768,0.0208876,0.00002426797],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.903914,0.009528396,0.01675479,0.03168738,0.0003021307,0.0001730569,0.0172433,0.0002602707,0.0201366],"genre_scores_gemma":[0.9840551,0.002338339,0.008763641,0.0006488448,0.0001489106,0.00009915043,0.002962678,0.00001751218,0.0009658668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1746707,"threshold_uncertainty_score":0.347308,"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."}}