Serious infections in patients with myasthenia gravis: population‐based cohort study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To characterize the frequency and risk of serious infections in patients with myasthenia gravis (MG) relative to age/sex/area-matched comparators. METHODS: This was a population-based cohort study in Ontario, Canada of patients with newly-diagnosed MG and 1:4 age/sex/area-matched general population comparators accrued from 1 April 2002 to 31 December 2015. The main outcome was a serious infection, defined by a primary diagnosis code on a hospitalization or emergency department record. We computed crude overall and sex-specific rates of infection among patients with MG and comparators, and the frequency of specific types of infection. Adjusted hazard ratios and 95% confidence intervals were estimated using Cox regression. RESULTS: Among 3823 patients with MG, 1275 (33.4%) experienced a serious infection compared with 2973/15 292 (19.4%) of comparators over a mean follow-up of over 5 years. Crude infection rates among patients with MG were twice those in comparators (72.5 vs. 35.0 per 1000 person-years, respectively). The most common infection types were respiratory infections, particularly bacterial pneumonia. After adjustment for potential confounders, MG was associated with a 39% increased infection risk (adjusted hazard ratio, 1.39; 95% confidence intervals, 1.28-1.51). CONCLUSIONS: Patients with MG are at a significantly higher absolute and relative risk of serious infections compared with age/sex/area-matched comparators. This needs to be considered when selecting MG treatments and when planning vaccination/prophylaxis. Determining whether this risk is due to the use of immunosuppressive medications (vs. MG itself) is an important area for future research.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".