{"id":"W4413794792","doi":"10.3390/make7030089","title":"AlzheimerRAG: Multimodal Retrieval-Augmented Generation for Clinical Use Cases","year":2025,"lang":"en","type":"article","venue":"Machine Learning and Knowledge Extraction","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Generative grammar; Search engine indexing; Artificial intelligence; Information retrieval; Machine learning; Natural language processing; Data science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002793151,0.001441633,0.0004959397,0.00218814,0.0003140015,0.001188111,0.001519682,0.001663063,0.01413454],"category_scores_gemma":[0.01363446,0.0003742181,0.001021141,0.0008710827,0.0005836257,0.001435698,0.002461324,0.0008416064,0.00372514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005697164,"about_ca_system_score_gemma":0.0006524527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001834733,"about_ca_topic_score_gemma":0.003153862,"domain_scores_codex":[0.9981045,0.001016684,0.0001514283,0.0002927264,0.0003604613,0.00007414134],"domain_scores_gemma":[0.9940295,0.004434654,0.0002621742,0.0007296685,0.0003732289,0.0001707548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001565672,0.0006227285,0.006405838,0.001704895,0.0003214376,0.003427352,0.0018127,0.03094689,0.03546431,0.006301072,0.08236136,0.8290658],"study_design_scores_gemma":[0.0009597381,0.00138497,0.006007454,0.0003768716,0.0003484323,0.005607114,0.001438946,0.748071,0.06357861,0.04330418,0.1286842,0.0002385283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08725765,0.002669536,0.7681709,0.00238084,0.000384068,0.002319104,0.009175791,0.1141263,0.01351587],"genre_scores_gemma":[0.3519394,0.0007953241,0.6267868,0.001176724,0.0001461727,0.001214624,0.01025711,0.001738438,0.005945403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01413454,"threshold_uncertainty_score":0.04728478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1080196297560264,"score_gpt":0.4138889138627881,"score_spread":0.3058692841067616,"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."}}