{"id":"W224850807","doi":"10.24095/hpcdp.32.4.02","title":"Features of physician services databases in Canada","year":2012,"lang":"en","type":"article","venue":"Chronic diseases and injuries in Canada","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Saskatchewan","funders":"Canadian Institutes of Health Research; Ministry of Health, Saskatchewan; Department of Health, Western Cape Government; Ottawa Hospital Research Institute; University of Saskatchewan; McGill University","keywords":"Comparability; Database; Medical diagnosis; Remuneration; Medicine; Family medicine; Coding (social sciences); Health information; Health care; Business; Computer science; Political science; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003327597,0.0003384068,0.0003687839,0.01101089,0.002799888,0.003401707,0.002365712,0.0003587325,0.005470737],"category_scores_gemma":[0.02461918,0.0004008137,0.0002992438,0.0474989,0.0005914053,0.001189096,0.001470558,0.0003575126,0.0009353008],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04137276,"about_ca_system_score_gemma":0.07144736,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9846243,"about_ca_topic_score_gemma":0.9799722,"domain_scores_codex":[0.9922318,0.0007246014,0.001115505,0.0008181822,0.004171279,0.000938574],"domain_scores_gemma":[0.9605563,0.005607638,0.006563133,0.001938035,0.0221046,0.003230381],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000395839,0.00009412756,0.7461324,0.00138315,0.0001447442,0.0003673856,0.002862668,0.001656307,0.000747246,0.005130645,0.1231543,0.1179312],"study_design_scores_gemma":[0.00004640524,0.00002369447,0.8782031,0.0004418778,0.00004829699,0.0002748411,0.002287273,0.001955934,0.0006900052,0.0005882516,0.1153829,0.00005745943],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2772668,0.004475851,0.002864541,0.004435476,0.00009217173,0.001214597,0.6517283,0.001183519,0.05673873],"genre_scores_gemma":[0.7087342,0.003238082,0.009715914,0.000924949,0.0000683284,0.000482169,0.2707347,0.0001195062,0.00598214],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9966724,"threshold_uncertainty_score":0.3001818,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04574532726809107,"score_gpt":0.3690624848333791,"score_spread":0.323317157565288,"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."}}