{"id":"W4380608300","doi":"10.1097/ccm.0000000000005967","title":"Flow-Sizing Critical Care Resources*","year":2023,"lang":"en","type":"article","venue":"Critical Care Medicine","topic":"Sepsis Diagnosis and Treatment","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alchemy (Canada)","funders":"NIH Clinical Center; Eisai; Vanderbilt University Medical Center; Texas Heart Institute; University of Minnesota; Memorial Sloan-Kettering Cancer Center; University of Southern California; National Institutes of Health; Ohio State University; National Cancer Institute; Emory University; Cleveland Clinic; Vanderbilt University; National Heart, Lung, and Blood Institute; University of Texas MD Anderson Cancer Center; Hospital for Sick Children; Pfizer; AcelRx Pharmaceuticals; Medical Center, University of Pittsburgh; University of Pittsburgh; Eli Lilly and Company","keywords":"Surge Capacity; Medicine; Health care; Sizing; Critical appraisal; Intensive care; Operations management; Medical emergency; Intensive care medicine; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.008746058,0.0004803611,0.0002222784,0.004716728,0.001133435,0.002732301,0.001174124,0.0004670962,0.005209316],"category_scores_gemma":[0.02695347,0.0001971911,0.000757777,0.004810212,0.001392062,0.004622968,0.002394084,0.0006503177,0.000277992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005749743,"about_ca_system_score_gemma":0.01816163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01351379,"about_ca_topic_score_gemma":0.01685706,"domain_scores_codex":[0.9951233,0.002049757,0.0008120103,0.0004600126,0.0010553,0.0004996029],"domain_scores_gemma":[0.9771319,0.009293282,0.007219857,0.001102848,0.004315474,0.0009365827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002448532,0.0001982543,0.3725593,0.007494913,0.0002294185,0.0003347576,0.009807597,0.00971747,0.001770394,0.1020737,0.03102297,0.4645464],"study_design_scores_gemma":[0.0001798885,0.000869614,0.5684192,0.01867018,0.000349273,0.001193618,0.04370284,0.017327,0.005695418,0.07978979,0.2635807,0.0002225293],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.624199,0.0318484,0.1323535,0.04663728,0.001056988,0.00809687,0.01253829,0.0007219996,0.1425477],"genre_scores_gemma":[0.9301432,0.005331454,0.0560381,0.002347331,0.0002541423,0.001902249,0.002154625,0.00003540425,0.001793527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01351379,"threshold_uncertainty_score":0.0462541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1188914719219904,"score_gpt":0.4241268679777759,"score_spread":0.3052353960557854,"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."}}