{"id":"W7117868885","doi":"10.2196/80433","title":"Applications of Machine Learning for Cognitive Health in Older Individuals With HIV: Rapid Systematic Review","year":2025,"lang":"en","type":"article","venue":"JMIR Aging","topic":"HIV Research and Treatment","field":"Immunology and Microbiology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; National Institute of Mental Health; National Institute of Nursing Research; National Heart, Lung, and Blood Institute; National Institute on Aging","keywords":"Dementia; Neurocognitive; Cognition; Leverage (statistics); Disease; Cognitive decline; MEDLINE","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004093541,0.00008091574,0.0004049061,0.0001483625,0.00009752389,0.000004274289,0.00007404643,0.00002949053,0.00005303507],"category_scores_gemma":[0.00006312376,0.00005788141,0.00003887543,0.0002373199,0.00003742287,0.00002314499,0.00002683507,0.0001443204,0.00004040415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003659894,"about_ca_system_score_gemma":0.0001005745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003164483,"about_ca_topic_score_gemma":0.00002324258,"domain_scores_codex":[0.9991289,0.0002304103,0.0002687571,0.0001493045,0.00002187556,0.0002007421],"domain_scores_gemma":[0.9993588,0.0003384728,0.0001271173,0.0001089997,0.0000540512,0.00001256626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001873035,0.00164132,0.08644878,0.8366544,0.002615964,0.000005369161,0.003963649,0.00001126335,0.000733861,0.001854233,0.005502233,0.06038168],"study_design_scores_gemma":[0.02656024,0.001774316,0.03060511,0.8891463,0.001193502,0.00007943508,0.004543034,0.00007570819,0.006286346,0.0003049004,0.03860433,0.0008267907],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.009374073,0.892623,0.0480012,0.009300587,0.00005255107,0.03680092,0.0007389004,0.0001488959,0.002959874],"genre_scores_gemma":[0.9866194,0.002836944,0.0001766308,0.000497546,0.000003051443,0.006460418,0.0009933697,0.00001071062,0.002401895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9772454,"threshold_uncertainty_score":0.2360335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01316323610124263,"score_gpt":0.3337496131633345,"score_spread":0.3205863770620919,"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."}}