Characterizing cognitive function during relapse in multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To characterize neuropsychological (NP) test performance during multiple sclerosis (MS) relapse and recovery. METHODS: Clinical status was assessed with NP testing and Expanded Disability Status Scale (EDSS) in 24 relapsing patients, and 24 individually-matched, stable controls. All presented with cognitive symptoms as indicated by patient, clinician or caregiver perceived decline, but were free of optic neuritis, ataxia and upper extremity weakness that could compromise NP testing. The presence of enhancing magnetic resonance imaging (MRI) lesions was considered confirmatory of relapse. Relapsing patients were treated with corticosteroids. NP testing and EDSS were compared to pre-relapse baseline levels, and three-month, post-relapse, follow-up. RESULTS: Analyses revealed significant decline on the Symbol Digit Modalities Test (SDMT) (p=0.005) and worsening on EDSS (p=0.019). Impairment was observed at the point of relapse in cases but not controls. The groups were no longer different at three-month follow-up. The increment of decline on SDMT was 3.5 raw score points, or roughly 6%. CONCLUSIONS: This is the first study to assess NP status changes during MS relapse using well established, reliable metrics. The presence of a clinically meaningful event is substantiated by decline in NP testing, observed or reported cognitive change, and in a subset of patients, gadolinium-enhancing MRI lesions.
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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 | 0.003 |
| 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".