{"id":"W1567025466","doi":"10.22329/amr.v12i3.658","title":"Multimorbidity Clusters: Clustering Binary Data From Multimorbidity Clusters: Clustering Binary Data From a Large Administrative Medical Database","year":2009,"lang":"en","type":"article","venue":"Applied Multivariate Research","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":128,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Health Services Research and Development; U.S. Department of Veterans Affairs","keywords":"Multimorbidity; Cluster (spacecraft); Cluster analysis; Data mining; Set (abstract data type); Medicine; Computer science; Comorbidity; Artificial intelligence; Psychiatry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.01078138,0.0005347117,0.000798507,0.007729798,0.001706194,0.002944006,0.0008913195,0.0006760061,0.001103375],"category_scores_gemma":[0.05249448,0.00040139,0.000878599,0.009436173,0.0008308522,0.00159682,0.002770995,0.00125903,0.0004417752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001825468,"about_ca_system_score_gemma":0.003773723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01344154,"about_ca_topic_score_gemma":0.01348001,"domain_scores_codex":[0.9885904,0.007220575,0.0008878419,0.0009268462,0.002016936,0.0003572406],"domain_scores_gemma":[0.982165,0.01023693,0.002214826,0.002502769,0.002441696,0.0004387709],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008088332,0.0007497038,0.5043339,0.001259663,0.0008592029,0.0003176675,0.007302821,0.01982943,0.002201407,0.02325611,0.01930484,0.4197765],"study_design_scores_gemma":[0.0003609927,0.0006694811,0.5836422,0.00099111,0.0004389857,0.001118385,0.01379672,0.2476082,0.005692088,0.1037363,0.04148585,0.0004596671],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5784836,0.001572116,0.3911372,0.004189811,0.0002407247,0.003538189,0.01131934,0.001694368,0.007824691],"genre_scores_gemma":[0.5437664,0.0003674892,0.4437357,0.0001954136,0.00009198297,0.00179018,0.009424416,0.0001076759,0.0005207261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01344154,"threshold_uncertainty_score":0.05701804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3888789705240165,"score_gpt":0.4888106898086551,"score_spread":0.09993171928463862,"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."}}