Diagnostic value of brain MRI and <sup>18</sup>F‐FDG PET in the differentiation of parkinsonian type multiple system atrophy from Parkinson’s disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Differentiation between parkinsonian type multiple system atrophy (MSA-P) and Parkinson's disease (PD) is important but often difficult. We investigated the diagnostic value of brain magnetic resonance imaging (MRI) and (18)F-fluorodeoxyglucose positron emission tomography ((18)F-FDG PET) in differentiating MSA-P from PD. METHODS: Twenty-four patients with MSA-P (16 probable and 8 possible) and eight patients with PD were included in this study. RESULTS: For analysis using the putaminal findings, the sensitivities were 58.3% by visual analysis of brain MRI, 95.8% by visual analysis of (18)F-FDG PET, and 79.2% by statistical parametric mapping (SPM) analysis of (18)F-FDG PET in differentiating MSA-P from PD; the specificity was 100% for each analysis. Using the putaminal findings, visual analysis of (18)F-FDG PET had a higher sensitivity compared with brain MRI (P = 0.004) and SPM analysis of (18)F-FDG PET revealed a tendency towards higher sensitivity compared with brain MRI (P = 0.063). For analysis using both putaminal and infratentorial findings, the sensitivities were 79.2% by visual analysis of brain MRI, 95.8% by visual analysis of (18)F-FDG PET, 95.8% by SPM analysis of (18)F-FDG PET in differentiating MSA-P from PD; the specificity was 100% for each analysis. CONCLUSION: Both brain MRI and (18)F-FDG PET showed diagnostic usefulness in differentiating MSA-P from PD, with (18)F-FDG PET being more sensitive than brain MRI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".