Post-acute sequela of COVID-19 infection in individuals with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Many common symptoms in post-acute sequelae following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC) overlap with those of multiple sclerosis (MS). We examined symptoms and performance of the PASC score, developed in the general population, in MS based on infection history. METHODS: We surveyed North American Research Committee on Multiple Sclerosis (NARCOMS) registry participants regarding infections and categorized participants based on infection history. Symptoms experienced before, during, and after infection were used to identify persistent new symptoms. PASC was defined as a score ⩾ 12 based on the National Institutes of Health (NIH) study RECOVER. RESULTS: Of 4787 participants surveyed, 2927 were included: 294 (10%) having recent COVID-19; 853 (29.1%) recent non-COVID-19 infection; 246 (8.4%) recent COVID-19 and non-COVID-19 infection; 1534 (52.4%) uninfected, defined as never having COVID-19 nor any infection within the past 6 months. Compared to those uninfected, infection groups reported at least a two-fold increase in fever, cough, loss of smell/taste, and shortness of breath. Based on persistent new symptoms, PASC was identified in only 1.5% of participants with COVID-19. CONCLUSION: Our study suggests lower than expected prevalence of PASC in MS and a complex association between infections and development of new persistent symptoms following infections. The similar proportions classified with PASC across infection groups shows that symptoms of PASC are common and complicate assessment of PASC in MS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".