{"id":"W2162713207","doi":"10.1136/bmjqs-2012-001170","title":"Method for developing national quality indicators based on manual data extraction from medical records","year":2012,"lang":"en","type":"article","venue":"BMJ Quality & Safety","topic":"Clinical practice guidelines implementation","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University","funders":"","keywords":"Usability; Medical record; Medicine; Data extraction; Data collection; Quality (philosophy); Reliability (semiconductor); Scale (ratio); Data quality; Medical emergency; MEDLINE; Data science; Operations management; Computer science; Power (physics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06921002,0.002032718,0.001357829,0.01274834,0.001596273,0.00296324,0.002723976,0.00163797,0.008037794],"category_scores_gemma":[0.1649119,0.001231894,0.001973991,0.01178182,0.001458203,0.002592895,0.004608623,0.002514758,0.005737121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002403796,"about_ca_system_score_gemma":0.01353706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004526092,"about_ca_topic_score_gemma":0.004773369,"domain_scores_codex":[0.9147435,0.04038351,0.02042252,0.007005041,0.01642652,0.001018776],"domain_scores_gemma":[0.8670514,0.05262266,0.01535121,0.01803461,0.0459476,0.00099251],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006419762,0.0009643115,0.02982028,0.005284199,0.0003018542,0.000314441,0.004831528,0.002152212,0.011353,0.02643948,0.03090969,0.886987],"study_design_scores_gemma":[0.003565386,0.004139153,0.1456119,0.006555316,0.001571816,0.002625596,0.01234011,0.1050025,0.08579285,0.08669849,0.5443429,0.001754022],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008699641,0.0002678816,0.9375483,0.001192737,0.0003350308,0.03918275,0.005484616,0.002392139,0.004896902],"genre_scores_gemma":[0.01114985,0.0001092118,0.9574541,0.0001862858,0.0000423227,0.02792429,0.002257359,0.00009560415,0.0007808503],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9307899,"threshold_uncertainty_score":0.3660219,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6252631282024464,"score_gpt":0.6800622710439453,"score_spread":0.05479914284149889,"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."}}