{"id":"W4243110913","doi":"10.36227/techrxiv.12146286","title":"NODE-RED Microservice Bedside System for Outcome Prediction of Patients with Suspected SEPSIS: Useful Study for the Coronavirus Outbreak and Beyond (Stage 1 of the Work)","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Outbreak; Stage (stratigraphy); Coronavirus disease 2019 (COVID-19); Septic shock; Sepsis; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Outcome (game theory); Work (physics); Coronavirus; Medicine; Intensive care medicine; Computer science; Virology; Internal medicine; Engineering; Biology; Infectious disease (medical specialty); Disease; Economics","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":[0.002203482,0.0006802061,0.0007584781,0.0003972557,0.0001914167,0.001061618,0.001018025,0.0005826812,0.00663194],"category_scores_gemma":[0.004416841,0.0001610983,0.0003456561,0.0004366916,0.0001274015,0.00118511,0.0003777866,0.0005903018,0.002654969],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003318173,"about_ca_system_score_gemma":0.000917908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005273111,"about_ca_topic_score_gemma":0.006435236,"domain_scores_codex":[0.9993982,0.0002773256,0.00004233946,0.00009679692,0.000117046,0.00006840476],"domain_scores_gemma":[0.9974697,0.0009398671,0.0001198462,0.0003939369,0.0007510237,0.0003256943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00575216,0.002245794,0.5500953,0.0005401949,0.0004182663,0.000478363,0.0002246012,0.03290788,0.008009916,0.001276074,0.07112081,0.3269306],"study_design_scores_gemma":[0.0006108626,0.004376166,0.234237,0.0003941033,0.000628776,0.0004489237,0.0008137828,0.7181537,0.01283993,0.00391074,0.02346163,0.000124337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8854548,0.002459499,0.06845933,0.006624069,0.001091708,0.0008853141,0.0197409,0.008016114,0.007268113],"genre_scores_gemma":[0.9292001,0.0008733047,0.0460956,0.0007663707,0.0003024848,0.0004979717,0.01776975,0.0002336331,0.004260879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00663194,"threshold_uncertainty_score":0.02218604,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04579728267117693,"score_gpt":0.2969805269432206,"score_spread":0.2511832442720436,"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."}}