{"id":"W2892196296","doi":"10.23889/ijpds.v3i4.627","title":"Comparing five comorbidity indices to predict mortality in chronic kidney disease","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Humber River Regional Hospital; University of Ottawa; Western University; Institute for Clinical Evaluative Sciences","funders":"","keywords":"Comorbidity; Medicine; Kidney disease; Renal function; Dialysis; Internal medicine; Population; Charlson comorbidity index; Logistic regression; Intensive care medicine; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01023321,0.0009769859,0.0007170075,0.002414767,0.0005788474,0.001366404,0.0009548827,0.0005917803,0.001361452],"category_scores_gemma":[0.02758779,0.0002259857,0.001519303,0.001499776,0.0005365921,0.000685688,0.001532767,0.0006955267,0.0001882329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002276857,"about_ca_system_score_gemma":0.001687915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02993404,"about_ca_topic_score_gemma":0.02609714,"domain_scores_codex":[0.9948042,0.002467562,0.0004067001,0.0005262842,0.001373091,0.0004222499],"domain_scores_gemma":[0.9857788,0.00773863,0.002512502,0.0007027371,0.002227576,0.001039768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000655787,0.00004194654,0.9916412,0.00002924224,0.000443992,0.00000723576,0.00003577334,0.0008454521,0.00004480738,0.00006073937,0.0002492885,0.005944566],"study_design_scores_gemma":[0.000108909,0.0007326961,0.9781859,0.0000542729,0.0004234256,0.00006462324,0.0001516804,0.01914931,0.0001949282,0.0003404639,0.0005692198,0.00002454247],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949581,0.0008758528,0.001603797,0.0002312468,0.00004451132,0.00009973614,0.001197505,0.00002453413,0.0009648012],"genre_scores_gemma":[0.9968088,0.0001769711,0.001561868,0.00004268344,0.00002459386,0.00005134577,0.001207392,0.00000473854,0.0001215176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02993404,"threshold_uncertainty_score":0.05951965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1559991138764423,"score_gpt":0.4554737720692009,"score_spread":0.2994746581927586,"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."}}