{"id":"W3017593937","doi":"10.1101/2020.04.19.20071639","title":"Optimizing COVID-19 surveillance in long-term care facilities: a modelling study","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Université de Versailles Saint-Quentin-en-Yvelines; Public Health England; Agence Nationale de la Recherche; Canadian Institutes of Health Research; National Institute for Health Research Health Protection Research Unit; National Institute for Health and Care Research; Conservatoire National des Arts et Métiers; Université Paris-Saclay; Alliance Nationale pour les Sciences de la Vie et de la Santé; Institut National de la Santé et de la Recherche Médicale; University of Oxford","keywords":"Medicine; Outbreak; Transmission (telecommunications); Pooling; Asymptomatic; Long-term care; Infection control; Coronavirus disease 2019 (COVID-19); Epidemiology; Emergency medicine; Cumulative incidence; Intensive care medicine; Pediatrics; Internal medicine; Disease; Virology; Cohort; Computer science; Nursing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.001630301,0.001354789,0.001001806,0.0006826151,0.0006038745,0.001516032,0.002076137,0.002555265,0.002128038],"category_scores_gemma":[0.005073154,0.000581129,0.001498653,0.0008229418,0.0007941498,0.0009549746,0.001016531,0.001329324,0.0001867904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003548152,"about_ca_system_score_gemma":0.002498207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08057602,"about_ca_topic_score_gemma":0.03889628,"domain_scores_codex":[0.9990221,0.0004487514,0.00003368271,0.0001615288,0.00005670788,0.0002772022],"domain_scores_gemma":[0.9935726,0.004689718,0.0006133774,0.00017425,0.0004989951,0.000451044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001187833,0.0001711621,0.004891425,0.0000338006,0.00004324433,0.00009043095,0.00002608085,0.9928953,0.0002540617,0.0003562476,0.000172837,0.0009465904],"study_design_scores_gemma":[0.00004621272,0.0001814606,0.001845499,0.000007801558,0.00003226392,0.00001732579,0.00007229523,0.9972606,0.0001324536,0.0002860251,0.0001060624,0.00001203087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.986689,0.0002668008,0.00946082,0.0003869259,0.00002106005,0.000105948,0.0008021364,0.00007269901,0.002194557],"genre_scores_gemma":[0.9945549,0.0001348771,0.003839241,0.00004732569,0.000008421512,0.00008925169,0.0003643009,0.00001104297,0.0009505877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08057602,"threshold_uncertainty_score":0.1602141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1204636939334777,"score_gpt":0.4140630369830458,"score_spread":0.2935993430495681,"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."}}