{"id":"W3184445017","doi":"10.3390/diagnostics11081309","title":"Forecasting COVID-19 Severity by Intelligent Optical Fingerprinting of Blood Samples","year":2021,"lang":"en","type":"article","venue":"Diagnostics","topic":"COVID-19 Clinical Research Studies","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Horizon 2020; Fundação para a Ciência e a Tecnologia; Engineering and Physical Sciences Research Council; Centre hospitalier universitaire Sainte-Justine","keywords":"Triage; Coronavirus disease 2019 (COVID-19); Fingerprint (computing); Medicine; Intensive care; Intensive care unit; Disease; Computer science; Emergency medicine; Artificial intelligence; Intensive care medicine; Internal medicine","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":[0.001314671,0.0007434906,0.0005941687,0.001509171,0.0001563795,0.000910311,0.0002841336,0.0004824247,0.0004872782],"category_scores_gemma":[0.003021721,0.0001679896,0.000560336,0.0007814845,0.0001454458,0.0004997558,0.0003689773,0.000509552,0.0002814742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002204985,"about_ca_system_score_gemma":0.000302571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001434139,"about_ca_topic_score_gemma":0.001879577,"domain_scores_codex":[0.9995078,0.0001740919,0.00005207683,0.0001150966,0.00008808215,0.00006289704],"domain_scores_gemma":[0.9987155,0.0004963158,0.0003973549,0.00009721465,0.000215623,0.00007789152],"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":[0.001003617,0.0003754845,0.7590419,0.0001618668,0.0003101189,0.0002402956,0.0001042506,0.03590175,0.04492364,0.0002894158,0.001420856,0.1562267],"study_design_scores_gemma":[0.00004927752,0.001247742,0.349338,0.00009274071,0.0003838913,0.0007303427,0.0002510955,0.612804,0.03131568,0.001774123,0.001923212,0.00008980212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9128738,0.002150541,0.08116762,0.0004183452,0.0001202753,0.00009963656,0.001293445,0.0004272239,0.001449039],"genre_scores_gemma":[0.9810929,0.0005302318,0.01731801,0.00009604463,0.00006886345,0.000028467,0.0006012031,0.00001060933,0.00025366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001509171,"threshold_uncertainty_score":0.006952703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1278443480046252,"score_gpt":0.4232187182999953,"score_spread":0.2953743702953701,"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."}}