{"id":"W4394807540","doi":"10.3390/jpm14040421","title":"Identifying Progression-Specific Alzheimer’s Subtypes Using Multimodal Transformer","year":2024,"lang":"en","type":"article","venue":"Journal of Personalized Medicine","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Information and Intelligent Systems; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; BioClinica; F. Hoffmann-La Roche; University of Southern California; Biogen; Meso Scale Diagnostics; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; National Institute on Aging; Alzheimer's Association; Canadian Institutes of Health Research; National Science Foundation","keywords":"Interpretability; Neuroimaging; Disease; Mechanism (biology); Computer science; Receiver operating characteristic; Machine learning; Artificial intelligence; Medicine; Neuroscience; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001336714,0.0002085033,0.0005472175,0.0006309958,0.0001021111,0.00005366738,0.0001192095,0.00008667522,0.003817272],"category_scores_gemma":[0.00008846085,0.0001284447,0.0003080297,0.0004730271,0.0003235332,0.0002960755,0.00001396359,0.0006114386,0.00003202561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001203933,"about_ca_system_score_gemma":0.0003605487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001139195,"about_ca_topic_score_gemma":7.143818e-7,"domain_scores_codex":[0.9972008,0.00008804546,0.0006634727,0.0002305417,0.001439541,0.0003775907],"domain_scores_gemma":[0.9987637,0.0001379734,0.0001399874,0.0001100783,0.0004789421,0.0003693013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003739575,0.0006294777,0.01509127,0.00120357,0.002965212,0.008027257,0.00830517,0.000001945488,0.6300352,0.0007311844,0.01510508,0.3141651],"study_design_scores_gemma":[0.04738908,0.01242942,0.07210553,0.04620537,0.01132242,0.03085116,0.02245793,0.006604208,0.08597808,0.001737865,0.6615946,0.001324332],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7958668,0.1721218,0.0157784,0.009821322,0.001808742,0.0007232748,0.000006206936,0.00006860618,0.00380484],"genre_scores_gemma":[0.9888958,0.003673906,0.004209365,0.000286679,0.002011519,0.000006113018,0.000009550516,0.0000466672,0.0008603428],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6464896,"threshold_uncertainty_score":0.9970934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09764416521624941,"score_gpt":0.4296251004604063,"score_spread":0.3319809352441569,"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."}}