{"id":"W4406962150","doi":"10.1002/alz.14398","title":"Predicting conversion in cognitively normal and mild cognitive impairment individuals with machine learning: Is the CSF status still relevant?","year":2025,"lang":"en","type":"article","venue":"Alzheimer s & Dementia","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Associazione Italiana Ricerca Alzheimer; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; National Institute on Aging; Alzheimer's Association","keywords":"Alzheimer's Disease Neuroimaging Initiative; Neuroimaging; Neuropsychology; Cerebrospinal fluid; Cognition; Cohort; Artificial intelligence; Machine learning; Cognitive impairment; Psychology; Medicine; Internal medicine; Computer science; Psychiatry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.00281324,0.0008890683,0.0007012375,0.00125821,0.0002843298,0.001221732,0.0006334281,0.0006354577,0.001017239],"category_scores_gemma":[0.008248652,0.0001417318,0.0007405318,0.0005269555,0.0003556823,0.0006834493,0.0006927341,0.0007426285,0.0002370935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006613469,"about_ca_system_score_gemma":0.0009059246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003575556,"about_ca_topic_score_gemma":0.004378148,"domain_scores_codex":[0.9993973,0.0002730195,0.00006078704,0.0001326957,0.00008063149,0.00005564446],"domain_scores_gemma":[0.9969168,0.002077173,0.0003678782,0.000171894,0.0002922126,0.000173968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001695893,0.0006064711,0.6566476,0.0002752721,0.0006582616,0.0004282179,0.0002161213,0.107264,0.002145587,0.0009970195,0.002652521,0.226413],"study_design_scores_gemma":[0.00009019845,0.0007723176,0.1353568,0.0001857283,0.0002584193,0.000446993,0.0001909472,0.8453329,0.003945756,0.01227043,0.001085673,0.00006382891],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9688493,0.00122912,0.02602666,0.001328898,0.00004646142,0.00008175304,0.0008865931,0.0002844897,0.001266772],"genre_scores_gemma":[0.9927788,0.0001373722,0.006358418,0.00009227321,0.0000261798,0.00001833603,0.000419938,0.000004862389,0.0001638085],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003575556,"threshold_uncertainty_score":0.01487797,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01654515939034683,"score_gpt":0.2901522509049861,"score_spread":0.2736070915146392,"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."}}