{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001204948,0.000826558,0.0009327443,0.0003718152,0.001367385,0.0004367975,0.001128365,0.0004450945,0.0006731293],"category_scores_gemma":[0.004200431,0.0006168601,0.00008886325,0.0009689031,0.0001003201,0.000410957,0.0006447016,0.00149496,0.00001046222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019614,"about_ca_system_score_gemma":0.002167911,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004499352,"about_ca_topic_score_gemma":0.03092715,"domain_scores_codex":[0.9946165,0.000381367,0.001172499,0.0013636,0.001411145,0.001054865],"domain_scores_gemma":[0.992266,0.0046293,0.0009871278,0.001494371,0.0002918807,0.0003313078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.003893731,0.0003981178,0.007492412,0.01071555,0.0008197965,0.0002638337,0.1760419,0.0007543014,0.0007633182,0.002501155,0.001507738,0.7948481],"study_design_scores_gemma":[0.01149722,0.007615789,0.05902616,0.02099212,0.005434856,0.001256743,0.4600289,0.3427106,0.0005860209,0.005566469,0.076449,0.008836113],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768316,0.01041516,0.0007267449,0.004805257,0.0004469031,0.005274582,0.0002649376,0.0002150441,0.001019742],"genre_scores_gemma":[0.9641616,0.005195301,0.01322204,0.0002025502,0.0003484855,0.001255954,0.01198173,0.0003778458,0.003254536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.786012,"threshold_uncertainty_score":0.9999327,"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."}}