Vaccinations and the Risk of Relapse in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: There has been some concern that vaccination may precipitate the onset of multiple sclerosis or lead to relapses. Since the recent hepatitis B vaccination program in France, there have been new reports of an increased risk of active multiple sclerosis after vaccination. METHODS: We conducted a case-crossover study to assess whether vaccinations increase the risk of relapse in multiple sclerosis. The subjects were patients included in the European Database for Multiple Sclerosis who had a relapse between 1993 and 1997. The index relapse was the first relapse confirmed by a visit to a neurologist and preceded by a relapse-free period of at least 12 months. Information on vaccinations was obtained in a standardized telephone interview and confirmed by means of medical records. Exposure to vaccination in the two-month risk period immediately preceding the relapse was compared with that in the four previous two-month control periods for the calculation of relative risks, which were estimated with the use of conditional logistic regression. RESULTS: Of 643 patients with relapses of multiple sclerosis, 15 percent reported having been vaccinated during the preceding 12 months. The reports of 94 percent of these vaccinations were confirmed. Of all the patients, 2.3 percent had been vaccinated during the preceding two-month risk period as compared with 2.8 to 4.0 percent who were vaccinated during one or more of the four control periods. The relative risk of relapse associated with exposure to any vaccination during the previous two months was 0.71 (95 percent confidence interval, 0.40 to 1.26). There was no increase in the specific risk of relapse associated with tetanus, hepatitis B, or influenza vaccination (range of relative risks, 0.22 to 1.08). Analyses based on risk periods of one and three months yielded similar results. CONCLUSIONS: Vaccination does not appear to increase the short-term risk of relapse in multiple sclerosis.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".