Effect of Alzheimer’s Disease and Lewy Body Disease on Metabolic Changes
Bibliographic record
Abstract
BACKGROUND: The relationship among amyloid-β (Aβ) deposition on amyloid positron emission tomography (PET), cortical metabolism on 18F-fluoro-2-deoxy-D-glucose (FDG)-PET, and clinical diagnosis has not been elucidated for both Alzheimer's disease (AD) and Lewy body disease (LBD). OBJECTIVE: We investigated the patterns of cerebral metabolism according to the presence of AD and LBD. METHODS: A total of 178 subjects were enrolled including 42 pure AD, 32 pure LBD, 34 Lewy body variant AD (LBVAD), 15 LBD with amyloid, 26 AD with dementia with Lewy bodies (DLB), and 29 control subjects. Pure AD, LBVAD, and AD with DLB groups had biomarker-supported diagnoses of typical AD, while pure LBD, LBD with amyloid, and AD with DLB groups had biomarker-supported diagnoses of typical LBD. Typical AD and LBD with amyloid showed amyloid-positivity on 18F-florbetaben (FBB) PET, while typical LBD and LBVAD had abnormalities on dopamine transporter PET. We measured regional patterns of glucose metabolism using FDG-PET and evaluated their relationship with AD and LBD. RESULTS: Compared with control group, typical AD and typical LBD commonly exhibited hypometabolism in the bilateral temporo-parietal junction, precuneus, and posterior cingulate cortex. Typical AD showed an additional hypometabolism in the entorhinal cortex, while patients with dopamine transporter abnormality-supported diagnosis of LBD showed diffuse hypometabolism that spared the sensory-motor cortex. Although the diffuse hypometabolism in LBD also involved the occipital cortex, prominent occipital hypometabolism was only seen in LBD with amyloid group. CONCLUSION: Combining clinical and metabolic evaluations may enhance the diagnostic accuracy of AD, LBD, and mixed disease cases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".