{"id":"W4405969760","doi":"10.1101/2024.12.31.24319830","title":"Integrating plasma, MRI, and cognitive biomarkers for personalized prediction of decline across cognitive domains","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Bundesministerium für Bildung, Wissenschaft und Forschung; H. Lundbeck A/S; Servier; Pfizer; Novartis Pharmaceuticals Corporation; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Eisai; Alzheimer's Association","keywords":"Cognition; Cognitive decline; Psychology; Medicine; Neuroscience; Internal medicine; Dementia; Disease","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":[0.002740254,0.0009204079,0.0009139599,0.00138888,0.0001748808,0.00156494,0.0004015782,0.0006614865,0.001200015],"category_scores_gemma":[0.004302798,0.0002617996,0.0007065073,0.0007616593,0.0002592504,0.0006130513,0.0004911567,0.0006560089,0.0003972534],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005016195,"about_ca_system_score_gemma":0.0009897858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002466093,"about_ca_topic_score_gemma":0.002851159,"domain_scores_codex":[0.9994231,0.0003429181,0.000030629,0.00009800465,0.00006991884,0.00003534573],"domain_scores_gemma":[0.9984326,0.0009147734,0.0002457928,0.0001276213,0.0001885481,0.00009074892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002689061,0.001405951,0.4771212,0.0006000078,0.00175413,0.000381244,0.0001798444,0.07951214,0.025529,0.0009736497,0.004835867,0.405018],"study_design_scores_gemma":[0.0002831825,0.001667217,0.2328739,0.0002497364,0.001464949,0.0007835723,0.0001736268,0.709907,0.03093701,0.01514812,0.006367082,0.0001447053],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8268643,0.009553131,0.1518862,0.003229498,0.0001817843,0.0002125908,0.002602746,0.00158837,0.003881374],"genre_scores_gemma":[0.9464931,0.001212911,0.05009577,0.0003042239,0.0001694059,0.00008025178,0.0006648448,0.0000397581,0.0009396546],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002740254,"threshold_uncertainty_score":0.01449198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03104191026460783,"score_gpt":0.3682454031854296,"score_spread":0.3372034929208217,"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."}}