{"id":"W4413480325","doi":"10.1093/braincomms/fcag168","title":"Scalable biological-cognitive profiling for Alzheimer’s disease in the population","year":2025,"lang":"en","type":"article","venue":"Brain Communications","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Janssen Biotech; MSD Australia; Genentech; Bristol-Myers Squibb Canada; International Institute of Information Technology, Hyderabad; Sigrid Juséliuksen Säätiö; Maze Therapeutics; Intellectual Property Office; Sanofi; GlaxoSmithKline; Merck; Centre of Excellence and Applied Sport Science Research, Queensland Academy of Sport; Celgene; Biogen; Pfizer; AstraZeneca; Bristol-Myers Squibb; Business Finland; Helsingin Yliopisto; Boehringer Ingelheim","keywords":"Profiling (computer programming); Scalability; Disease; Alzheimer's disease; Cognition; Computer science; Population; Computational biology; Neuroscience; Medicine; Biology; Internal medicine; Programming language; Environmental health","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":[0.001569456,0.0004295222,0.0003207906,0.001296375,0.0004187198,0.000552957,0.0004963651,0.0005506256,0.00148766],"category_scores_gemma":[0.003828708,0.0001322833,0.0002758295,0.0009814949,0.0002282497,0.0003545602,0.0006069622,0.0003762542,0.0005824899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002765929,"about_ca_system_score_gemma":0.0003504668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003176229,"about_ca_topic_score_gemma":0.003661365,"domain_scores_codex":[0.999139,0.0003792583,0.00005448224,0.0001739532,0.0001969235,0.00005644224],"domain_scores_gemma":[0.9990072,0.0001815466,0.0002795203,0.0001340886,0.0002904251,0.0001072545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002884617,0.0002317888,0.9767729,0.0000413102,0.00008336864,0.0001528238,0.0001581598,0.0003054624,0.004637058,0.0001147422,0.001153162,0.01606082],"study_design_scores_gemma":[0.00002359775,0.000474461,0.994683,0.00001357509,0.00004720988,0.000522827,0.0001870595,0.001004327,0.001529626,0.000413261,0.001087257,0.00001389561],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.988583,0.0005604459,0.00516421,0.0002393047,0.00002681649,0.0001943989,0.002579157,0.00009443744,0.002558216],"genre_scores_gemma":[0.9953537,0.0001493013,0.002701801,0.0001189432,0.00002752019,0.0001491849,0.001038108,0.000006436056,0.0004550891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003176229,"threshold_uncertainty_score":0.008300126,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1826187669610715,"score_gpt":0.3837851963335712,"score_spread":0.2011664293724997,"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."}}