{"id":"W2776711118","doi":"10.14236/jhi.v24i4.962","title":"The Multimorbidity Cluster Analysis Tool: Identifying Combinations and Permutations of Multiple Chronic Diseases Using a Record-Level Computational Analysis","year":2017,"lang":"en","type":"article","venue":"Journal of Innovation in Health Informatics","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Université de Sherbrooke; Western University; Centre for Family Medicine","funders":"","keywords":"Multimorbidity; Executable; Computer science; Data science; Sample (material); Cluster (spacecraft); Medical diagnosis; Diagnosis code; Java; Comorbidity; Health care; Chronic disease; Medicine; Family medicine; Pathology; Population; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001704401,0.0001026378,0.000440311,0.001568869,0.0005814196,0.0001874364,0.000154301,0.0000341325,0.00001687088],"category_scores_gemma":[0.001721717,0.00008167722,0.0001314773,0.001919389,0.0001616036,0.0007573011,0.00007188012,0.0001851659,3.88607e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003729468,"about_ca_system_score_gemma":0.0008526681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001723757,"about_ca_topic_score_gemma":0.000245556,"domain_scores_codex":[0.9962057,0.0000578504,0.003030939,0.00006348023,0.0004810046,0.0001610122],"domain_scores_gemma":[0.9933819,0.0003477971,0.004724548,0.0002737487,0.001211957,0.00006011247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001007174,0.0002276254,0.7348137,0.001119547,0.002330251,0.000002758956,0.001231226,0.248132,0.000003377442,0.00620734,0.0002357787,0.005595695],"study_design_scores_gemma":[0.001417445,0.00004358379,0.5182632,0.0001168925,0.0006936227,0.000003592704,0.0007438599,0.4780845,8.965953e-7,0.0004989939,0.00009281878,0.00004062547],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7923203,0.0001091014,0.2053474,0.001646929,0.0001350504,0.0003204965,0.00006052989,0.000005672635,0.00005453106],"genre_scores_gemma":[0.980832,0.0001435702,0.01869274,0.0001667082,0.00003932737,0.000004988059,0.00009568177,0.000005385977,0.00001957912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2299525,"threshold_uncertainty_score":0.4471868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1517890825353758,"score_gpt":0.4286937438238665,"score_spread":0.2769046612884907,"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."}}