Relapse recovery in multiple sclerosis: Effect of treatment and contribution to long-term disability
Bibliographic record
Abstract
BACKGROUND: Although recovery from relapses in MS appears to contribute to disability, it has largely been ignored as a treatment endpoint and disability predictor. OBJECTIVE: To identify demographic and clinical predictors of relapse recovery in the first 3 years and examine its contribution to 10-year disability and MRI outcomes. METHODS: Relapse recovery was retrospectively assessed in 360 patients with MS using the return of the Expanded Disability Status Scale (EDSS), Functional System Scale and neurologic signs to baseline at least 6 months after onset. Univariate and multivariable models were used to associate recovery with demographic and clinical factors and predict 10-year outcomes. RESULTS: Recovery from relapses in the first 3 years was better in patients who were younger, on disease-modifying treatment, with a longer disease duration and without bowel or bladder symptoms. For every incomplete recovery, 10-year EDSS increased by 0.6 and 10-year timed 25-foot walk increased by 0.5 s. These outcomes were also higher with older age and higher baseline BMI. Ten-year MRI brain atrophy was associated only with older age, and MRI lesion volume was only associated with smoking. CONCLUSIONS: Early initiation of disease-modifying treatment in MS was associated with improved relapse recovery, which in turn prevented long-term disability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".