{"id":"W2313622815","doi":"10.1097/00004479-200207000-00002","title":"Outpatient Encounter Data for Risk Adjustment","year":2002,"lang":"en","type":"article","venue":"Journal of Ambulatory Care Management","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Victoria Park","funders":"","keywords":"Payment; Incentive; Audit; Medical diagnosis; Business; Health care; Actuarial science; Quality (philosophy); Data quality; Service (business); Medical emergency; Medicine; Finance; Marketing; Economics; Accounting","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":[0.002120682,0.0002061928,0.0004620117,0.0002519895,0.0004304105,0.00001240442,0.0007665131,0.0001391397,0.0004465928],"category_scores_gemma":[0.0001239666,0.0001481871,0.0001450711,0.0001293982,0.00002263844,0.0002380134,0.0003176289,0.0007194762,0.0001768768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001814027,"about_ca_system_score_gemma":0.0001532698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004570635,"about_ca_topic_score_gemma":0.00009809116,"domain_scores_codex":[0.9962749,0.0005194411,0.001527439,0.0003173153,0.0006640506,0.0006968826],"domain_scores_gemma":[0.9963809,0.0002741937,0.001540935,0.00105455,0.0005069153,0.0002425039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001871225,0.0002632972,0.01374924,0.003464272,0.0005832236,0.00003694699,0.008440135,0.0001543009,0.000004624537,0.0007145914,0.8383245,0.1340778],"study_design_scores_gemma":[0.002394479,0.0007769782,0.005642308,0.0005172908,0.0002470234,0.000004900595,0.01033405,0.0009061524,0.000002037496,0.00007435198,0.9789277,0.000172706],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4790019,0.1428135,0.06216483,0.01306831,0.1108444,0.04141297,0.002321956,0.0005840321,0.1477881],"genre_scores_gemma":[0.9704445,0.004422923,0.00683708,0.003843798,0.004438274,0.0003026764,0.00006141959,0.0001312873,0.009518023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4914426,"threshold_uncertainty_score":0.6042895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08602176242565995,"score_gpt":0.4077485946527339,"score_spread":0.321726832227074,"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."}}