{"id":"W4409383178","doi":"10.1101/2025.04.09.25325520","title":"Predicting Future Brain Atrophy Based on Longitudinal MRI","year":2025,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"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; Commonwealth Scientific and Industrial Research Organisation; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Atrophy; Neuroscience; Medicine; Physical medicine and rehabilitation; Psychology; Pathology","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":[0.001496249,0.0005505033,0.0003271926,0.0007555885,0.0001207685,0.0005236724,0.0003480885,0.0004317797,0.0005981771],"category_scores_gemma":[0.003579172,0.0001743023,0.0005328491,0.0003443857,0.0001523514,0.0005918404,0.0003985155,0.0005556491,0.0004645318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002384598,"about_ca_system_score_gemma":0.0003748196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005527131,"about_ca_topic_score_gemma":0.007992367,"domain_scores_codex":[0.9998243,0.00005669926,0.00001710684,0.00005954949,0.0000215034,0.00002079249],"domain_scores_gemma":[0.9987133,0.0005812043,0.00028738,0.0001171945,0.0002289,0.00007196204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006430753,0.0002533209,0.7543828,0.00007206942,0.0002599786,0.0002973194,0.0001074825,0.1764129,0.003531879,0.0002701496,0.001724813,0.06204414],"study_design_scores_gemma":[0.00001833178,0.0003280109,0.1871876,0.00004583484,0.0001063179,0.0002660831,0.00007548907,0.8071886,0.002491345,0.001361654,0.0008999112,0.00003069579],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9735323,0.0006247795,0.02229036,0.0003022117,0.00002794032,0.00002603661,0.002320804,0.0002972517,0.0005783848],"genre_scores_gemma":[0.9896436,0.0002690048,0.006715307,0.0000367945,0.00002565379,0.00002606511,0.002764882,0.00001145569,0.000507347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005527131,"threshold_uncertainty_score":0.01098996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03425632368688262,"score_gpt":0.282592085000299,"score_spread":0.2483357613134164,"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."}}