Quantifying cognition and fatigue to enhance the sensitivity of the EDSS during relapses
Bibliographic record
Abstract
Background: Cognition is affected by relapses in persons with multiple sclerosis (PwMS), yet the Expanded Disability Status Scale (EDSS) does not readily detect cognitive changes. Objective: The objective of this study is to improve the detection of cognitive decline during relapses, by incorporating the Symbol Digit Modalities Test (SDMT) into the cerebral Functional System Score (CFSS) of the EDSS. Methods: This prospective study recruited PwMS from three dedicated MS centers. All subjects had EDSS, SDMT, and Fatigue Severity Scale (FSS) administered. Subjects experiencing a relapse were assigned to the relapse group (RG). Matched controls from the larger cohort were assigned to the stable group (SG). RG and SG subjects underwent the same evaluation at relapse and 3 months later. Our main outcomes were a modified CFSS (m-CFSS) and modified EDSS (m-EDSS), incorporating SDMT and FSS, accounting for cognitive performance and fatigue rating, during relapse. Results: The full cohort included 592 subjects; 80 qualified for RG and 72 were matched to the SG. The m-CFSS was significantly higher than CFSS at baseline (median = 2 vs. median = 0, p < 0.001) and relapse (median = 2 vs. median = 1, p < 0.001). The m-EDSS was higher than EDSS (median 3.0 vs. 2.5, p = 0.02) at relapse, where 35 RG subjects (43.8%) had higher m-EDSS than EDSS at relapse. Conclusion: This study demonstrates that incorporating the SDMT and FSS improves the accuracy of the EDSS, by accounting for cognitive changes, during relapse activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".