{"id":"W4386647851","doi":"10.1038/s41598-023-41984-8","title":"Red blood cell distribution width for the prediction of outcomes after cardiac arrest","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Inflammatory Biomarkers in Disease Prognosis","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Gottfried und Julia Bangerter-Rhyner-Stiftung; Mach-Gaensslen Foundation of Canada; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Red blood cell distribution width; Distribution (mathematics); Blood cell; Cardiology; Medicine; Internal medicine; Computer science; Mathematics","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":[0.001220933,0.0005194462,0.0004157067,0.001120553,0.0001959394,0.000652016,0.0003135623,0.0004515753,0.001410784],"category_scores_gemma":[0.004462466,0.0001119729,0.0004186031,0.0008046167,0.0002570944,0.0004319577,0.0004889849,0.0006950032,0.000338113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002110194,"about_ca_system_score_gemma":0.0002541079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001187713,"about_ca_topic_score_gemma":0.001269278,"domain_scores_codex":[0.9996599,0.0001485549,0.00004275783,0.00005489334,0.00005570835,0.00003825177],"domain_scores_gemma":[0.997086,0.001062894,0.001148829,0.0001489654,0.0002061796,0.0003471285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005667903,0.00002968422,0.9902351,0.00002909906,0.00009160018,0.00005037667,0.00002470217,0.0003491569,0.0004078281,0.00003954921,0.0001453142,0.008030922],"study_design_scores_gemma":[0.00001920834,0.0003656593,0.9940334,0.00003678544,0.0001088379,0.0002236762,0.00008672601,0.00405332,0.0004300607,0.0002367433,0.0003915766,0.00001400036],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950596,0.002747611,0.0008934647,0.0001377015,0.00003673481,0.00001290053,0.0004037613,0.00002223467,0.0006860154],"genre_scores_gemma":[0.9988417,0.0003254842,0.0003562096,0.0000134407,0.00003531354,0.000007818832,0.0002743305,0.000003182632,0.000142565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001410784,"threshold_uncertainty_score":0.006456971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01330534816296221,"score_gpt":0.2498544520137018,"score_spread":0.2365491038507396,"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."}}