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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".