{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007479424,0.0001209946,0.0001577104,0.0001275844,0.0003480354,0.0001933812,0.0001095084,0.000112299,0.000005000451],"category_scores_gemma":[0.001212004,0.0001169488,0.00007006459,0.0001700661,0.00002142311,0.0004075048,0.00008336196,0.0003223855,0.000007184413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003231649,"about_ca_system_score_gemma":0.00005760236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007864054,"about_ca_topic_score_gemma":0.0000620603,"domain_scores_codex":[0.9987769,0.0002181431,0.0003242418,0.0004443771,0.00007522097,0.000161123],"domain_scores_gemma":[0.998742,0.0007865272,0.0001011718,0.0001898284,0.0001256619,0.00005485306],"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":[0.0001647077,0.0003173353,0.051008,0.0000620313,0.0001268805,0.000008202534,0.0004604133,0.001324034,0.007086067,0.006259376,0.001813876,0.9313691],"study_design_scores_gemma":[0.0007864939,0.0001034726,0.004399339,0.00002357531,0.00003114754,0.00001322474,0.00001162667,0.9421145,0.0006421285,0.00007066025,0.05169103,0.0001127601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1948111,0.00183088,0.8007352,0.0004135992,0.001453609,0.0002201033,0.000001364054,0.0002028577,0.000331214],"genre_scores_gemma":[0.9722927,0.0002045755,0.02287147,0.00006201213,0.0003317504,0.0000131435,0.00002281854,0.000008853011,0.004192668],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9407905,"threshold_uncertainty_score":0.4769034,"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."}}