{"id":"W2900518013","doi":"10.1371/journal.pone.0207468","title":"Use of physician billing claims to identify infections in children","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; Hospital for Sick Children; Pediatric Oncology Group; University Health Network; University of Toronto; SickKids Foundation; Institute for Clinical Evaluative Sciences","funders":"Canadian Institutes of Health Research; Department of Family and Community Medicine, University of Toronto; University of Toronto; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences","keywords":"Medicine; Medical record; Medical diagnosis; Diagnosis code; Respiratory tract infections; Population; Otitis; Electronic medical record; Internal medicine; Pediatrics; Predictive value; Emergency medicine; Surgery; Respiratory system; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007948272,0.00007896023,0.0002469229,0.0001748192,0.00002996494,0.00001123854,0.0000659051,0.00003220453,0.00006880887],"category_scores_gemma":[0.0002161752,0.00007791277,0.00003935319,0.0004518524,0.00006365616,0.0001209545,0.00005730705,0.00008998488,0.0002983478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003851933,"about_ca_system_score_gemma":0.00003637092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002493154,"about_ca_topic_score_gemma":0.0002045705,"domain_scores_codex":[0.9992,0.00002915526,0.0001977364,0.0001910568,0.0002215297,0.0001604482],"domain_scores_gemma":[0.9992794,0.00003634112,0.00005189571,0.0003904107,0.0001398453,0.0001021176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004601044,0.00199303,0.9123369,0.00004243408,0.0001872662,0.000002142498,0.0001215185,0.000005523634,0.08387975,0.00002976433,0.0004865305,0.0008691003],"study_design_scores_gemma":[0.0004214863,0.0001927636,0.972559,0.0004094803,0.00008898922,0.00000107755,0.000007865689,0.0001110121,0.02593088,0.00004926795,0.0001427888,0.00008539792],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987454,0.00001907199,0.00005618929,0.0001768231,0.00002810732,0.0004113373,0.0001259275,0.0000652767,0.0003717983],"genre_scores_gemma":[0.9978534,0.00001481135,0.001228562,0.0004654487,0.000232284,0.00001784895,0.00008207736,0.00001857416,0.00008696824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06022207,"threshold_uncertainty_score":0.3834758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06785607498185837,"score_gpt":0.3071043052741106,"score_spread":0.2392482302922522,"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."}}