{"id":"W4391224062","doi":"10.3233/shti231121","title":"Ensemble Clustering to Generate Phenotypes of Kidney Transplant Donors and Recipients","year":2024,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Renal Transplantation Outcomes and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Cluster analysis; Categorical variable; Computer science; Cluster (spacecraft); Data mining; Phenotype; Kidney transplant; Kidney transplantation; Artificial intelligence; Medicine; Kidney; Machine learning; Biology; Internal medicine; Genetics; Gene","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":[0.002406951,0.0005084792,0.0005851276,0.001640795,0.0004098791,0.0007546429,0.0005845117,0.0004378607,0.0007367862],"category_scores_gemma":[0.007435102,0.0001725515,0.0007723108,0.000830929,0.0002310793,0.0005715194,0.0007342387,0.0006740405,0.0002690145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006554651,"about_ca_system_score_gemma":0.0006291506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003569523,"about_ca_topic_score_gemma":0.004275991,"domain_scores_codex":[0.9990957,0.0003950617,0.00005041564,0.0002130566,0.000177669,0.00006815137],"domain_scores_gemma":[0.9970858,0.001180128,0.0003224269,0.0005562215,0.0007602493,0.00009519322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007582491,0.0003828721,0.2268842,0.0001622035,0.0005943709,0.0003481708,0.0009932831,0.3998668,0.01537294,0.009415511,0.008919455,0.3363019],"study_design_scores_gemma":[0.00003951432,0.0001416253,0.04080036,0.00002869652,0.00005778201,0.0001823866,0.0002689871,0.9375765,0.003937338,0.0142437,0.002676297,0.00004677795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4096499,0.0002136113,0.5838467,0.0004449249,0.00009065201,0.0002944992,0.002521722,0.0009596686,0.001978209],"genre_scores_gemma":[0.8308226,0.00008235036,0.1632231,0.0001167166,0.00002761474,0.0001804257,0.004834162,0.00009280223,0.0006201006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003569523,"threshold_uncertainty_score":0.01272935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04511658915336087,"score_gpt":0.3661169518222915,"score_spread":0.3210003626689306,"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."}}