{"id":"W2978219667","doi":"10.14309/00000434-201510001-02299","title":"Geographic Information System to Determine Quality of Bowel Preparation for a Catchment Area of a Veterans Affairs Healthcare System: A Descriptive Analysis","year":2015,"lang":"en","type":"article","venue":"The American Journal of Gastroenterology","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Colonoscopy; Veterans Affairs; Bowel preparation; Catchment area; Geographic information system; Health care; Colorectal cancer; General surgery; Internal medicine; Cancer; Drainage basin","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":[0.00136456,0.0002007337,0.0003339168,0.006337083,0.0003144131,0.0006570338,0.0004235645,0.0001403319,0.00256627],"category_scores_gemma":[0.006294082,0.0001625145,0.0007904352,0.00745608,0.0002530859,0.0004604097,0.0008062684,0.000239773,0.0001899578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001059061,"about_ca_system_score_gemma":0.001463612,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03534324,"about_ca_topic_score_gemma":0.01914,"domain_scores_codex":[0.9988354,0.000261344,0.0003008915,0.0001294745,0.0003490917,0.0001238144],"domain_scores_gemma":[0.9950516,0.001538968,0.002061503,0.0002203941,0.0008605348,0.000267024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004963228,0.00001418034,0.9964648,0.00004497826,0.00006075584,0.00004459573,0.0001937252,0.0002573888,0.00007189857,0.00007096585,0.0003820553,0.002344926],"study_design_scores_gemma":[0.000009718279,0.00009131016,0.9951292,0.00003141123,0.00004730319,0.0001906321,0.001411592,0.001695087,0.0001442592,0.00006526471,0.001174224,0.00001002497],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983866,0.0001689316,0.001120496,0.0001074922,0.000006363566,0.0002097903,0.01283101,0.00005131512,0.001638634],"genre_scores_gemma":[0.9934947,0.00007822675,0.001461807,0.00001067652,0.000003939188,0.000153698,0.004661154,0.00000642664,0.0001294732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03534324,"threshold_uncertainty_score":0.07027501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03791166169700681,"score_gpt":0.314314748901778,"score_spread":0.2764030872047711,"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."}}