{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006700024,0.000382249,0.0007047578,0.00008229524,0.0002620738,0.0001178767,0.001854625,0.0001819529,0.000003726291],"category_scores_gemma":[0.0001624762,0.0002142235,0.0001715456,0.0003948257,0.00007490624,0.00009995022,0.001971556,0.0005341418,3.94396e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001274847,"about_ca_system_score_gemma":0.0001746465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001070069,"about_ca_topic_score_gemma":0.0006391931,"domain_scores_codex":[0.9969214,0.0003184803,0.0009926762,0.0008579774,0.0006044768,0.0003050537],"domain_scores_gemma":[0.9954127,0.0008964562,0.001140541,0.001629799,0.0008330271,0.00008749841],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003088489,0.0001572225,0.9894046,0.002851704,0.0002564362,3.978136e-7,0.003340383,0.002510742,0.00000938863,0.0005769781,0.00007755504,0.0005057935],"study_design_scores_gemma":[0.002062429,0.0005230075,0.9488184,0.0002766059,0.0001996026,6.821084e-7,0.001170226,0.0464053,0.00008013684,0.00006155982,0.0001953388,0.0002067548],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8471929,0.000186055,0.1291171,0.003301011,0.00173144,0.01631216,0.001702711,0.0003419803,0.0001146162],"genre_scores_gemma":[0.9887053,0.000001624932,0.01014864,0.000234652,0.00004945946,0.0005855283,0.00004816008,0.00004492366,0.0001817492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1415123,"threshold_uncertainty_score":0.8735783,"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."}}