The Value of GRE, ADC and Routine MRI in Distinguishing Parkinsonian Disorders
Bibliographic record
Abstract
OBJECTIVES: To study different radiological signs and sequences including apparent diffusion coefficient (ADC) and gradient echo (GRE) to differentiate degenerative parkinsonian syndromes. BACKGROUND: Multiple system atrophy (MSA), Parkinson's disease (PD), progressive supranuclear palsy (PSP) and corticobasal degeneration (CbD) differ in the pattern of neurodegeneration and cellular damage. Measuring the ADC, GRE sequences for paramagnetic substances and simple anatomical assessments have been reported individually to assist in separating some of these disorders, but have not been compared. METHODS: brain MRIs from May 2002 to February 2008 were retrospectively evaluated by raters blinded to the clinical diagnosis for predefined MRI signs on T1, T2 and GRE sequences. ADC values were quantitatively measured. Medical records were objectively analyzed using standard clinical criteria for different parkinsonian syndromes. RESULTS: 195 cases comprising of 61 PD, 15 MSA-P, 7 MSA-C, 21 PSP, 6 Corticobasal syndrome, 21 not fitting criteria and 64 controls were evaluated. 73% of patients with MSA-P had hypointensity of the putamen (compared to the pallidum) on GRE. The specificity of this sign to diagnose MSA-P was 90% versus PD and 76% versus PSP. When GRE hypointensity was combined with atrophy of the putamen the specificity improved to 98% (versus PD) and 95% (versus PSP) without altering the sensitivity. The ADC values were significantly higher in the middle cerebellar peduncle in cases with MSA-C versus controls, PD and PSP (p<0.001). CONCLUSIONS: The combination of hypointensity and atrophy of the putamen on GRE is useful in differentiating MSA-P from other parkinsonian syndromes.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".