{"id":"W7114917324","doi":"10.1016/j.dajour.2025.100667","title":"An adaptive learning framework for Alzheimer’s disease diagnosis using structural Magnetic Resonance Imaging data analytics","year":2025,"lang":"en","type":"article","venue":"Decision Analytics Journal","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Saskatchewan Polytechnic","funders":"","keywords":"Neuroimaging; Domain (mathematical analysis); Domain adaptation; Feature (linguistics); Class (philosophy); Adaptation (eye); Pattern recognition (psychology); Deep learning; Functional magnetic resonance imaging","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.001706979,0.0009365358,0.0008328524,0.0009518017,0.0003993748,0.0008324118,0.001744143,0.001187779,0.001377697],"category_scores_gemma":[0.002132154,0.0004150342,0.0009471547,0.0006436282,0.0007331242,0.0008986245,0.00156547,0.00168498,0.0006108583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008169338,"about_ca_system_score_gemma":0.001319005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008468054,"about_ca_topic_score_gemma":0.00881063,"domain_scores_codex":[0.9994518,0.0001479455,0.00002902108,0.0001926962,0.0001157315,0.0000627377],"domain_scores_gemma":[0.9994504,0.0002217728,0.00006432911,0.00004667847,0.0001683574,0.00004851677],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001364419,0.0001480653,0.002452325,0.00008478722,0.00009988871,0.0002046013,0.0001320419,0.819837,0.004093186,0.01187106,0.003453994,0.1574866],"study_design_scores_gemma":[0.000004063855,0.00001692391,0.0001067599,0.000004129749,0.000004989382,0.00001478693,0.000004027921,0.9963124,0.0002650132,0.002860006,0.0004030396,0.000003877487],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01399259,0.0007220238,0.9823155,0.0006301209,0.00005433201,0.00007198501,0.0001674155,0.001026045,0.001019933],"genre_scores_gemma":[0.6465729,0.001047601,0.3431574,0.0006950842,0.0002179168,0.0004761133,0.0009927532,0.000174783,0.006665424],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008468054,"threshold_uncertainty_score":0.01683754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1036282463531432,"score_gpt":0.4385273532928715,"score_spread":0.3348991069397284,"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."}}