Safety of influenza vaccination in patients with myasthenia gravis: A population‐based study
Bibliographic record
Abstract
Influenza vaccination has been associated with adverse events including Guillain-Barré syndrome. Because the safety of influenza vaccination in patients with myasthenia gravis (MG) has not been established, some clinicians discourage vaccination for these patients. We explored whether the administration of influenza vaccine to patients with MG might increase the risk of myasthenic crisis. Using population-based healthcare data from Ontario, Canada, from 1992 to 2007, we utilized the self-matched, case-series method of detecting adverse events following vaccination. We studied patients with established myasthenia who were hospitalized for MG within 42 weeks of influenza vaccination. We defined the primary risk interval as the 6 weeks following vaccination. Between January 1, 1992 and March 31, 2006, we identified 3667 hospital admissions for MG. No seasonal trend in MG admissions was evident. In 513 instances, hospitalization occurred within 42 weeks following vaccination in patients previously diagnosed with MG. Among these patients, 266 (52%) were men, the median age was 74 years, and 86 (17%) had previously undergone thymectomy. The estimated relative incidence of admission for MG in the primary risk interval compared with the control interval was 0.84 (95% confidence interval 0.65-1.09). We found similar results in stratified analyses according to gender, age, and thymectomy status. Vaccination of patients with MG against influenza was not found to be associated with exacerbations of the disease. Our findings do not support the practice of withholding influenza vaccination in patients with MG.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".