{"id":"W2468054471","doi":"10.1111/hdi.12445","title":"A comparison between physicians and computer algorithms for form CMS‐2728 data reporting","year":2016,"lang":"en","type":"article","venue":"Hemodialysis International","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Algorithm; Comorbidity; Diagnosis code; Hemodialysis; MEDLINE; Medical prescription; Medical record; Health care; Incidence (geometry); Dialysis; Pediatrics; Intensive care medicine; Internal medicine; Computer science; Population","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06646582,0.0006355924,0.0006982244,0.003847055,0.0004044244,0.00273168,0.001135788,0.0009534219,0.001576378],"category_scores_gemma":[0.2077615,0.0003518923,0.001460803,0.003644766,0.00072463,0.002406696,0.001375303,0.0007188394,0.0003583937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002150355,"about_ca_system_score_gemma":0.002519918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003615587,"about_ca_topic_score_gemma":0.002544289,"domain_scores_codex":[0.9199336,0.05348254,0.008540316,0.007107196,0.01007112,0.0008652254],"domain_scores_gemma":[0.6982365,0.2336049,0.03746389,0.01077874,0.01823432,0.001681635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004878495,0.0005609362,0.906808,0.0003944787,0.001364995,0.00003548612,0.00060338,0.005093921,0.000412662,0.001615135,0.002652236,0.07558024],"study_design_scores_gemma":[0.001484538,0.004929307,0.8912827,0.0004787318,0.0008711781,0.0005109147,0.001196956,0.08875568,0.0022112,0.001798446,0.00633423,0.0001460078],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9701408,0.001525713,0.01835075,0.001171894,0.0002288953,0.001159835,0.003223922,0.0003586074,0.003839576],"genre_scores_gemma":[0.9708153,0.0003850047,0.02441787,0.0004416905,0.00008310857,0.0005526163,0.002965173,0.0000721754,0.0002671121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06646582,"threshold_uncertainty_score":0.3515091,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1369542408168665,"score_gpt":0.4081485748931512,"score_spread":0.2711943340762847,"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."}}