{"id":"W4412166686","doi":"10.1017/cjn.2025.10144","title":"GR.4 EEG biomarkers for Alzheimer’s Disease: a novel automated pipeline for detecting and monitoring disease progression","year":2025,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Hospital Edmonton; Toronto Public Health","funders":"","keywords":"Disease; Pipeline (software); Electroencephalography; Disease monitoring; Medicine; Computer science; Neuroscience; Psychology; Pathology; Operating system","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001084194,0.001815887,0.0007463885,0.002469258,0.0004074927,0.001469578,0.0008350163,0.0009636754,0.006050833],"category_scores_gemma":[0.003058336,0.0004896109,0.0009872378,0.0009457123,0.0002516943,0.0006933084,0.0009452709,0.0007964256,0.005017242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005170004,"about_ca_system_score_gemma":0.001374907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004794937,"about_ca_topic_score_gemma":0.007054855,"domain_scores_codex":[0.9993653,0.000092544,0.0000447406,0.0002098117,0.0002187953,0.00006872742],"domain_scores_gemma":[0.9993006,0.0001762665,0.00009890796,0.000092795,0.000287518,0.00004385985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007837958,0.0004331252,0.02813394,0.0004043685,0.0003559847,0.0006056029,0.0001889568,0.01842028,0.1020644,0.001932089,0.02249375,0.8241838],"study_design_scores_gemma":[0.0001986143,0.0007712276,0.06170917,0.0001357354,0.0003044353,0.001465877,0.0001209711,0.7978927,0.1003427,0.008377606,0.02854318,0.0001377853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1180739,0.001788067,0.8011668,0.001047687,0.0002566684,0.001050613,0.009538156,0.06034415,0.006733834],"genre_scores_gemma":[0.3787574,0.0006773205,0.5992107,0.0003054494,0.000140735,0.0007559321,0.009716554,0.0008039764,0.0096319],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006050833,"threshold_uncertainty_score":0.02024204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06067740667977711,"score_gpt":0.3379245129417588,"score_spread":0.2772471062619817,"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."}}