{"id":"W4409316860","doi":"10.1117/12.3047109","title":"A comparison of biomarker modalities for predicting disease progression in dementia patients","year":2025,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Healthcare","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Dementia; Biomarker; Modalities; Disease; Computer science; Medicine; Internal medicine; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.004669185,0.001039605,0.0009728929,0.002051557,0.0002481444,0.001297578,0.0006395043,0.0008730253,0.0007144136],"category_scores_gemma":[0.01097978,0.0002538813,0.001042243,0.000614839,0.0002450123,0.001463946,0.0007290505,0.0007466296,0.0003340307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007439035,"about_ca_system_score_gemma":0.0006996841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004629322,"about_ca_topic_score_gemma":0.004387749,"domain_scores_codex":[0.9987497,0.0005961586,0.0001160969,0.0002331691,0.0001950789,0.0001097969],"domain_scores_gemma":[0.9963723,0.002139232,0.0003539705,0.0002162063,0.0007200522,0.0001982418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00810623,0.0007699992,0.7522488,0.0002041289,0.001460417,0.0002033176,0.0002399696,0.05969521,0.002180226,0.0005301422,0.001481476,0.1728801],"study_design_scores_gemma":[0.0001938212,0.004221873,0.2870936,0.000145028,0.0008639019,0.0004680837,0.000464886,0.6995829,0.004291547,0.001305416,0.001261212,0.0001076539],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9855366,0.001709096,0.00904042,0.0004066854,0.00007032425,0.0001021326,0.0008968077,0.0001758072,0.002061994],"genre_scores_gemma":[0.9942971,0.0003805362,0.004246215,0.0000596748,0.00003579383,0.00004880135,0.0005945012,0.000007694164,0.0003296437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004669185,"threshold_uncertainty_score":0.02469331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03265753162000896,"score_gpt":0.3964347535963981,"score_spread":0.3637772219763892,"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."}}