{"id":"W4408135273","doi":"10.3389/fnagi.2025.1542514","title":"Comparing machine learning classifier models in discriminating cognitively unimpaired older adults from three clinical cohorts in the Alzheimer’s disease spectrum: demonstration analyses in the COMPASS-ND study","year":2025,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Women and Children’s Health Research Institute; University of Alberta","funders":"","keywords":"Random forest; Artificial intelligence; Machine learning; Naive Bayes classifier; Computer science; Cognitive impairment; Probabilistic classification; Cohort; Classifier (UML); Cognition; Psychology; Medicine; Support vector machine; Neuroscience; Pathology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003169138,0.0002821456,0.0004612208,0.0006700301,0.0003602538,0.0005783957,0.002299904,0.0000531347,9.379214e-7],"category_scores_gemma":[0.0006791195,0.0002149675,0.00007851344,0.003261253,0.0003719081,0.001269115,0.0003902496,0.001097132,7.973296e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001110294,"about_ca_system_score_gemma":0.0001699013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002608754,"about_ca_topic_score_gemma":0.0113835,"domain_scores_codex":[0.9945361,0.002003491,0.0010034,0.001132342,0.0006907471,0.0006339],"domain_scores_gemma":[0.9979763,0.001022833,0.0002405366,0.0006405384,0.00004349496,0.00007627585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004556683,0.0006205607,0.9377751,0.000005731685,0.000004270439,0.0002520683,0.009854307,0.04594607,0.00001792806,0.000672026,0.00004309048,0.004763255],"study_design_scores_gemma":[0.0002893953,0.00003307684,0.4840012,0.0001307434,0.00001256444,7.185229e-7,0.006376047,0.5051622,0.00002268763,0.003857874,0.000001683042,0.0001117349],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.738,0.0003389246,0.2580646,0.001702998,0.00055461,0.0009655879,0.000002091936,0.00004378523,0.0003274198],"genre_scores_gemma":[0.9976496,0.00002053982,0.001514469,0.000684295,0.00002359998,0.00008827263,0.000003452131,0.000009339704,0.000006393664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4592161,"threshold_uncertainty_score":0.8766118,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1530823120286338,"score_gpt":0.3727973550559285,"score_spread":0.2197150430272947,"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."}}