Evaluation of Brainstem Involvement in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND/AIMS: the aim of the present study was to determine the optimum method to detect brainstem lesions in patients with Multiple Sclerosis (MS). METHODS: 72 patients with the diagnosis of relapsing-remitting MS were prospectively included. brainstem functional system score (bSfS) (part of the expanded disability status scale (edSS) evaluating brainstem symptomatology) was calculated. Magnetic resonance imaging (Mri) was performed on 1.5t and t1, t2, pd and fluid-attenuated inversion recovery (flair) sequences were analyzed for presence of brainstem lesions. auditory evoked potentials (aep) and ocular and cervical vestibular evoked myogenic potentials (oVeMp and cVeMp) were performed according to the standardized protocol. RESULTS: from 72 patients, 18 (25%) had clinical involvement of the brainstem. Mri showed brainstem involvement in 29 (40%) patients. of the neurophysiological tests, aep showed pathological result in 16 (22%) patients, oVeMp in 36 (50%) patients, cVeMp in 18 (25%) patients, and VeMp (combination of oVeMp and cVeMp) in 45 (63%) patients. VeMp detected brainstem lesions in higher percentage than clinical examination, Mri and aep, which was statistically significant (< 0.0001, 0.012 and < 0.0001, respectively). CONCLUSIONS: results of the present study have shown that VeMps are the optimal method to detect brainstem lesions in multiple sclerosis and that they detect them significantly better than clinical examination, aep or 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".