Impact of multiple sclerosis relapse: The NARCOMS participant perspective
Bibliographic record
Abstract
Acute relapses continue to be a significant aspect of multiple sclerosis (MS) on both the epidemiologic level and the individual patient level. Past work demonstrates residual disability from relapses as well as high patient-reported rates of ineffective relapse treatment. To better characterize the impact of MS relapses on the patient, a relapse-specific survey was administered through the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry to 1000 registry participants who had reported at least one relapse in the past 12 months. Thirty percent of respondents confirmed lack of relapse treatment efficacy at one month and at three months. Relapses also impacted socioeconomic measures; for individuals still going to school or working, more than half missed days and their average loss of school or work was 12.7 days. An impact on household tasks was reported by 68% of respondents. A healthcare facility such as a hospital, emergency room or urgent care center was utilized by 20.4% of respondents. The most common relapse symptoms were fatigue, weakness of the lower extremity, sensory symptoms, problems walking, and weakness of the upper extremity. Of the respondents who reported receiving corticosteroid treatment (53.3%), over half reported an adverse event. However, this was not a significant factor in dictating whether or not respondents would seek a different treatment on their next relapse, although 31% would choose a different treatment for their next relapse. Relapses continue to be an impactful experience that requires continued clinical attention. Improved follow-up from relapses and relapse treatment might be beneficial.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".