Recovery of cognitive function after relapse in multiple sclerosis
Bibliographic record
Abstract
Background: Cognitive impairment is common in multiple sclerosis (MS) but its manifestation as acute disease activity is underappreciated. Objective: The aim of this study is to examine recovery after MS relapse on multiple tests of cognitive and motor function and explore correlates of change with Expanded Disability Status Scale (EDSS), magnetic resonance imaging (MRI), and cognitive reserve. Methods: Fifty relapsing group (RG) and matched stable participants were examined at baseline, during relapse, and at 3-month follow-up. Tests of cognitive processing speed (Symbol Digit Modalities Test (SDMT)) and consensus opinion measures of memory, ambulation, and manual dexterity were administered. All RG patients were treated with a 5-day course of Acthar Gel (5 mL/80 IU). Results: In RG patients, SDMT declined from 55.2 to 44.6 at relapse and recovered to 51.7, a slope differing from stable controls ( p = 0.001). A statistical trend ( p = 0.07) for the same effect was observed for verbal memory and was significant for ambulation ( p = 0.03). The Cerebral Function Score from the EDSS also changed in the RG and recovered incompletely relative to controls ( p = 0.006). Conclusion: These results replicate earlier reports of cognitive worsening during relapse in MS. Clinically meaningful improvements followed relapse on SDMT and ambulation. Cognitive decline during relapse can be appreciated on neurological exam but not patient-reported outcomes.
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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) | 0.001 | 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".