{"id":"W4402842129","doi":"10.2196/53229","title":"Examining Racial Disparities in Colorectal Cancer Screening and the Role of Online Medical Record Use: Findings From a Cross-Sectional Study of a National Survey","year":2024,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Cancer Institute","keywords":"Preprint; Cross-sectional study; Colorectal cancer; Medicine; Health Information National Trends Survey; Gerontology; Psychology; Cancer; Internal medicine; Political science; World Wide Web; Health information; Computer science; Health care; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007868627,0.0001332294,0.0003630768,0.0001870226,0.00006742463,0.00005099181,0.00007703399,0.000131562,0.0002459171],"category_scores_gemma":[0.0004855263,0.0001004958,0.00005827894,0.0004674707,0.0002500177,0.0001456505,0.00008019299,0.0004255255,2.591748e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001690336,"about_ca_system_score_gemma":0.0004692748,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1103115,"about_ca_topic_score_gemma":0.1356353,"domain_scores_codex":[0.9980896,0.0001454703,0.0004713883,0.0003234737,0.0008153185,0.0001548111],"domain_scores_gemma":[0.998482,0.00108372,0.00009587987,0.00006708909,0.0002082647,0.00006300348],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0111193,0.0001313584,0.9742635,0.00004201357,0.0001752269,0.000005582097,0.001987753,0.0000739321,0.0003909466,0.00001656358,0.00004195196,0.01175181],"study_design_scores_gemma":[0.003378562,0.0005825946,0.9828745,0.0003913176,0.00003003521,0.000007491446,0.0004154462,0.01177101,0.0002925491,0.00005575133,0.0001083101,0.0000924016],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963086,0.002260294,0.00002688338,0.0001033108,0.000372187,0.0004591153,0.000396132,0.00003205127,0.00004148341],"genre_scores_gemma":[0.9989113,0.0001295348,0.00004508616,0.00004779424,0.0003779438,0.0003589299,0.00004389495,0.00001869672,0.00006677977],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0253238,"threshold_uncertainty_score":0.895613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.063862173007072,"score_gpt":0.3744086771629678,"score_spread":0.3105465041558958,"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."}}