Characteristics of cerebral glucose metabolism in patients with cognitive impairment in multiple system atrophy
Bibliographic record
Abstract
Objective: F-FDG PET imaging for evaluating the cognitive status of MSA patients. Methods: F-FDG PET imaging. All patients were subjected to the Mini-Mental State Examination and categorized into the MSA with normal cognition (MSA-NC) and MSA with cognitive impairment (MSA-CI) groups. Statistical parametric mapping (version 12) was used to analyze PET images and compare the differences in brain metabolism between the MSA and HC groups. The PET images of MSA-CI and MSA-NC patients were compared to analyze the metabolic characteristics, and the regional cerebral metabolic rate of glucose (rCMRglc) was calculated for different brain regions. Receiver operating characteristic (ROC) curves were used to analyze the ability of the rCMRglc of different brain regions to assess the cognitive status of MSA patients. Results: Compared with the HC group, the MSA group showed diffuse reductions in glucose metabolism in the cerebellar regions, decreased metabolism in specific areas of the left inferior parietal lobule, right putamen, and left middle temporal gyrus, and increased metabolism in the left postcentral gyrus, right postcentral gyrus, left precuneus. Compared with the MSA-NC group, the MSA-CI group exhibited decreased metabolism in the right superior frontal gyrus and right superior parietal lobule. The rCMRglc value of the right superior frontal gyrus (Montreal Neurological Institute coordinates: 18, -6, 70) showed better diagnostic efficacy for identifying MSA-CI, with an area under the ROC curve of 0.829 (95%CI = 0.696-0.963), sensitivity of 84.6% (95%CI = 66.5-93.9%), and specificity of 83.3% (95%CI = 60.8-94.2%). Conclusion: Compared with MSA-NC patients, the MSA-CI patients show decreased metabolism in the right superior frontal gyrus and right superior parietal lobule. The rCMRglc value of the right superior frontal gyrus may be a potential molecular imaging biomarker for diagnosing MSA-CI.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".