{"id":"W3021773640","doi":"10.1002/mus.26915","title":"Reply: The Optimal Management of Electrodiagnostic Studies during <scp>COVID</scp>‐19 Outbreak","year":2020,"lang":"en","type":"letter","venue":"Muscle & Nerve","topic":"Long-Term Effects of COVID-19","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"","keywords":"Pandemic; Staffing; Contingency plan; Coronavirus disease 2019 (COVID-19); Diagnostic test; Medical emergency; Medicine; Outbreak; Contingency; Resource (disambiguation); Intensive care medicine; Operations research; Psychology; Disease; Computer science; Emergency medicine; Computer security; Nursing; Pathology; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.003749228,0.0008190665,0.001142467,0.0008289049,0.002322336,0.002706086,0.002133936,0.0365548,0.009232005],"category_scores_gemma":[0.05314632,0.0007504952,0.001137135,0.0005418809,0.002656662,0.004665357,0.002002181,0.03577668,0.0086592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002804461,"about_ca_system_score_gemma":0.003763238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005008005,"about_ca_topic_score_gemma":0.003889526,"domain_scores_codex":[0.9969139,0.001077341,0.0005665857,0.0003515249,0.000627019,0.0004636042],"domain_scores_gemma":[0.9817042,0.009361207,0.001210552,0.000548411,0.005464918,0.001710804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002550523,0.00001137938,0.0003577208,0.00004987038,0.000007164128,0.0005091768,0.00009965714,0.00003239738,0.00006695413,0.0006374205,0.9951708,0.00303196],"study_design_scores_gemma":[0.00009350886,0.00009031326,0.001676151,0.0006973878,0.00003661947,0.003824507,0.001205656,0.0003754131,0.0004401183,0.005985689,0.9854369,0.0001379488],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0003200859,0.000813311,0.0001651033,0.9721016,0.02539312,0.00001324694,0.00009434995,0.00006806688,0.001031124],"genre_scores_gemma":[0.00264213,0.0009598819,0.0003303594,0.9641716,0.02889998,0.00004052357,0.000059121,0.0000416358,0.002854831],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.0365548,"threshold_uncertainty_score":0.03088415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.023899492455305,"score_gpt":0.2919477808247909,"score_spread":0.268048288369486,"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."}}