Infection, Relapses, and Pseudo-Relapses in Individuals With Multiple Sclerosis
Bibliographic record
Abstract
Background and Objectives: Infections are associated with an increased risk of relapse and pseudo-relapse in persons with multiple sclerosis (MS). However, the relationship with relapses and pseudo-relapses after SARS-CoV-2 infections (COVID) vs other infections in MS is poorly understood. Therefore, we compared the occurrence of relapse and pseudo-relapse after COVID and other infections with noninfected participants with MS. Methods: In spring 2023, we surveyed participants from the North American Research Committee on Multiple Sclerosis Registry regarding whether they had had a COVID infection, other infections, relapses, and pseudo-relapses. Recent infections, occurring in the 6 months before the survey, were used to categorize participants into groups: recent COVID, non-COVID infection (with no history of ever having COVID), COVID and non-COVID infections, or uninfected. Results: Of the 4,787 participants eligible for analysis, 2,927 participants were included, of whom 294 (10%) had a recent COVID infection; 853 (29.1%) had 1 recent infection other than COVID; 246 (8.4%) had a recent COVID and non-COVID infection; and 1,534 (52.4%) had no infection with COVID nor any infection within the past 6 months (uninfected). Compared with no infections, non-COVID infection was associated with a 39% increased likelihood of relapse (1.39, 95% CI [1.04-1.87]), whereas a recent COVID infection was associated with a decreased likelihood of relapse (0.45 [0.23, 0.87]), adjusting for covariates. All infection groups were associated with increased odds of pseudo-relapse compared with the uninfected group (non-COVID infections: 1.78 [1.44, 2.20]; COVID infection: 1.80 [1.32, 2.45]; COVID and non-COVID infection: 3.04 [2.24, 4.12]). Discussion: Because individuals with MS are at increased risk of infections, the association of infections with relapses and pseudo-relapses is clinically important. The high prevalence of acute worsening after infection, regardless of the type of infection, compared with those with no reported infection, needs to be considered in the management of persons with MS.
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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 | 0.002 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".