{"id":"W2148729053","doi":"10.1111/j.1475-6773.2006.00681.x","title":"Administrative Data Algorithms Can Describe Ambulatory Physician Utilization","year":2007,"lang":"en","type":"article","venue":"Health Services Research","topic":"Medication Adherence and Compliance","field":"Medicine","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; Institute for Clinical Evaluative Sciences","funders":"Banting and Best Diabetes Centre, University of Toronto; Canadian Institutes of Health Research; University of Toronto; Canadian Diabetes Association","keywords":"Medicine; Concordance; Ambulatory; Ambulatory care; Pharmacy; Family medicine; MEDLINE; Algorithm; Primary care; Health care; Computer science","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002657492,0.0001103578,0.0002220348,0.0001741095,0.0003187843,0.00003077646,0.0004869931,0.00007592046,0.0003517501],"category_scores_gemma":[0.00003613868,0.0001002051,0.00001864892,0.0008786148,0.0001295058,0.0001517853,0.0001589173,0.0004729389,0.0008335439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001835409,"about_ca_system_score_gemma":0.001436935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001700107,"about_ca_topic_score_gemma":0.0009923724,"domain_scores_codex":[0.9971954,0.0001635985,0.0003768782,0.0004816938,0.001109037,0.0006733665],"domain_scores_gemma":[0.9975569,0.0001363515,0.0001504721,0.001056566,0.0004959622,0.0006036962],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009318572,0.001799347,0.04193314,0.007187638,0.0001591579,0.000211847,0.01204591,0.000001562236,0.001637332,0.003859454,0.06246603,0.8677667],"study_design_scores_gemma":[0.002571864,0.001865331,0.5449919,0.00256725,0.00002356958,0.00005429638,0.04478501,0.01815376,0.003512435,0.0007529097,0.3803134,0.0004083724],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6907482,0.01856059,0.01810193,0.08075549,0.001611081,0.01277076,0.0006632097,0.0009893921,0.1757994],"genre_scores_gemma":[0.9776189,0.0007151877,0.002318483,0.01378774,0.0006015541,0.0000390906,0.001149865,0.00002899634,0.003740131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8673584,"threshold_uncertainty_score":0.9999444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5795032699931961,"score_gpt":0.5845965758318235,"score_spread":0.005093305838627327,"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."}}