{"id":"W7132869398","doi":"","title":"The Problem with Pertussis: Finding Undetected Pertussis Cases in Electronic Medical Record Primary Care (EMRPC) to Improve Data Accuracy and Burden Estimates","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Census and Population Estimation","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; Toronto Public Health","funders":"Canadian Institutes of Health Research","keywords":"Medical record; Public health surveillance; Primary care; Electronic medical record; Under-reporting; Electronic health record; Confidence interval; Public health; Cohort; Sensitivity (control systems)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.05175694,0.0006056565,0.0009664835,0.002395616,0.001045937,0.003299666,0.001924332,0.001296475,0.001276043],"category_scores_gemma":[0.22168,0.0009009191,0.00151876,0.004554066,0.0008839045,0.002448925,0.002209388,0.001440281,0.0005568949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002911078,"about_ca_system_score_gemma":0.004212196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05453655,"about_ca_topic_score_gemma":0.05038426,"domain_scores_codex":[0.9569018,0.02710095,0.003358967,0.003878493,0.007961,0.0007987759],"domain_scores_gemma":[0.8571677,0.09596462,0.01363643,0.02090778,0.01174411,0.0005793815],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004434451,0.0001717798,0.723471,0.0008791769,0.0007820001,0.0001830147,0.002103516,0.01120502,0.002021658,0.003838707,0.00846064,0.2464399],"study_design_scores_gemma":[0.0001999463,0.0007148489,0.8301161,0.001325497,0.0008644647,0.00102346,0.001375079,0.1168269,0.01258239,0.008675733,0.02614845,0.0001471145],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6499413,0.008511608,0.2864945,0.02159603,0.0005500296,0.002379871,0.01287796,0.001534127,0.01611454],"genre_scores_gemma":[0.8058487,0.001810193,0.184303,0.002584295,0.000186694,0.0005808393,0.002870986,0.00009445353,0.001720844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05453655,"threshold_uncertainty_score":0.2737201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03876207379667022,"score_gpt":0.3722596643241619,"score_spread":0.3334975905274916,"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."}}