{"id":"W4405980019","doi":"10.1101/2024.12.27.630495","title":"Subregional Biomarkers in FDG PET for Alzheimer’s Diagnosis and Staging: An Interpretable and Explainable model","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Boğaziçi Üniversitesi; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Pet imaging; Positron emission tomography; Medicine; Radiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001770423,0.0007977411,0.0005913276,0.0007009179,0.0002554079,0.0008647137,0.0006991639,0.0006756758,0.001325216],"category_scores_gemma":[0.00278975,0.0002647735,0.0008862587,0.0002995507,0.0004424654,0.0005381564,0.0005096892,0.0008108894,0.0003447186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005786576,"about_ca_system_score_gemma":0.0006130797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004592085,"about_ca_topic_score_gemma":0.002970894,"domain_scores_codex":[0.9997154,0.0001184909,0.00001042493,0.00009045983,0.00002970614,0.00003554102],"domain_scores_gemma":[0.9993246,0.0004331622,0.0001071158,0.00003714305,0.000071692,0.00002633312],"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":[0.0004888962,0.0002639073,0.03921115,0.000117906,0.0003005626,0.0004120532,0.0002290925,0.8648011,0.005704517,0.00722295,0.003079194,0.07816862],"study_design_scores_gemma":[0.000009196367,0.00002972247,0.002965316,0.000009528697,0.00001934995,0.00003483948,0.00001100705,0.9930459,0.0001703103,0.003506741,0.0001911453,0.000006973166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4315385,0.001499523,0.5596582,0.002455708,0.0001182607,0.0001501371,0.00115629,0.0009254485,0.00249801],"genre_scores_gemma":[0.9697742,0.0002352371,0.02737682,0.0001330638,0.00007525172,0.0001374263,0.0005149738,0.00003456926,0.001718365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004592085,"threshold_uncertainty_score":0.009362996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02513350126142693,"score_gpt":0.2824756085391699,"score_spread":0.2573421072777429,"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."}}