{"id":"W4280585809","doi":"10.2196/35973","title":"A Standard-Based Citywide Health Information Exchange for Public Health in Response to COVID-19: Development Study","year":2022,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public health; Public health informatics; Health information exchange; Public health surveillance; Health care; Health informatics; Business; Data governance; International Health Regulations; Medicine; Environmental health; Computer science; Health policy; HRHIS; Data quality; Disease; Nursing; Political science; Infectious disease (medical specialty)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02530666,0.0005696744,0.0002271428,0.001576251,0.001013227,0.002630217,0.002036844,0.0009285348,0.003613988],"category_scores_gemma":[0.01785961,0.0003954445,0.0006786333,0.002013624,0.001088326,0.003175875,0.002175067,0.0008941031,0.0008029056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007217451,"about_ca_system_score_gemma":0.01684432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04751094,"about_ca_topic_score_gemma":0.02586729,"domain_scores_codex":[0.9905297,0.005485891,0.0004660038,0.0007048997,0.001761794,0.001051819],"domain_scores_gemma":[0.9788643,0.005619895,0.001247188,0.002998506,0.009124247,0.002145973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00300828,0.02417829,0.4491638,0.0009894586,0.0002687642,0.001252791,0.008842758,0.04994697,0.01367822,0.04290103,0.02294344,0.3828263],"study_design_scores_gemma":[0.00188182,0.01928407,0.5805326,0.000633463,0.0004005414,0.0008080136,0.02596002,0.2514851,0.03086036,0.004587284,0.08325378,0.0003130273],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9472635,0.00007672833,0.02383955,0.001365614,0.00003664082,0.006552859,0.002996952,0.0008217265,0.01704652],"genre_scores_gemma":[0.845059,0.0001743684,0.1354634,0.0003302426,0.00001497358,0.003181793,0.01167359,0.0001136521,0.003988925],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.04751094,"threshold_uncertainty_score":0.133836,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06897556953694717,"score_gpt":0.3734336341856551,"score_spread":0.3044580646487079,"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."}}