{"id":"W4410188239","doi":"10.5005/jp-journals-10071-24971","title":"Bayesian Analysis of Modified Nutrition Risk in Critically Ill (mNUTRIC) Score for Mortality Prediction in Critically Ill Patients","year":2025,"lang":"en","type":"article","venue":"Indian Journal of Critical Care Medicine","topic":"Nutrition and Health in Aging","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Critically ill; Medicine; Intensive care medicine; Critical illness; Bayesian probability; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.02676691,0.001181097,0.002072021,0.004129477,0.0004720197,0.001936196,0.001148241,0.001109129,0.002577215],"category_scores_gemma":[0.07968613,0.0006148146,0.003378152,0.001880359,0.0005890308,0.00116471,0.001613833,0.001678572,0.0003838417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007164666,"about_ca_system_score_gemma":0.001596743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00584993,"about_ca_topic_score_gemma":0.00417278,"domain_scores_codex":[0.9832742,0.01336686,0.000761276,0.001288201,0.00102179,0.0002877009],"domain_scores_gemma":[0.9473887,0.04285373,0.005219224,0.001552977,0.002178645,0.0008066588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.005367803,0.0003082319,0.76592,0.001200554,0.009480891,0.0003461923,0.0005005478,0.08347621,0.001402343,0.005741056,0.004321937,0.1219342],"study_design_scores_gemma":[0.0005284232,0.001518088,0.2103844,0.001156595,0.005967629,0.0008576574,0.0002655978,0.7491456,0.000971988,0.02464731,0.004308196,0.0002485187],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7004967,0.01830305,0.2671618,0.002483837,0.0002289605,0.0008780838,0.005202333,0.0005925145,0.004652751],"genre_scores_gemma":[0.9606808,0.001293918,0.03505564,0.000210698,0.0001289896,0.0002823627,0.001968886,0.000046683,0.0003320878],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02676691,"threshold_uncertainty_score":0.1415586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03440040069253376,"score_gpt":0.3799243920625274,"score_spread":0.3455239913699936,"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."}}