{"id":"W4399817150","doi":"10.2196/53368","title":"COVID-19 Health Impact: A Use Case for Syndromic Surveillance System Monitoring Based on Primary Care Patient Registries in the Netherlands","year":2024,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"COVID-19 and Mental Health","field":"Psychology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Coronavirus disease 2019 (COVID-19); Public health surveillance; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Environmental health; Public health; Disease surveillance; Medicine; Medical emergency; Virology; Outbreak; Computer science; World Wide Web; 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":[0.03386325,0.0003886129,0.0007443917,0.002485616,0.0006109652,0.003132332,0.001512969,0.001085517,0.001341237],"category_scores_gemma":[0.08923875,0.0005206492,0.001018202,0.004393087,0.000610263,0.002268746,0.002354074,0.0006253723,0.0001520564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003969269,"about_ca_system_score_gemma":0.005151909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1647295,"about_ca_topic_score_gemma":0.1203696,"domain_scores_codex":[0.9571952,0.03210527,0.002973909,0.002610114,0.003919075,0.001196295],"domain_scores_gemma":[0.9282391,0.04019134,0.01458168,0.008100245,0.007669124,0.001218561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001808326,0.00006148784,0.9733788,0.0005623566,0.0003330823,0.0005275831,0.001716575,0.0006048227,0.0001331184,0.0006408829,0.001726687,0.02013374],"study_design_scores_gemma":[0.0001226809,0.0004234634,0.9658098,0.001067475,0.0006016192,0.001719262,0.005800639,0.01020835,0.0005349708,0.0004152828,0.01324299,0.00005350071],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9473007,0.007767971,0.007900851,0.003681438,0.0001852698,0.00169556,0.01417922,0.0001310289,0.01715794],"genre_scores_gemma":[0.9887978,0.00152728,0.00549527,0.0004198514,0.00004633018,0.0005563865,0.002598832,0.00002721884,0.0005309707],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1647295,"threshold_uncertainty_score":0.3275413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09312951473357503,"score_gpt":0.4207687016181574,"score_spread":0.3276391868845824,"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."}}