Rate of Recurrence of Adverse Events Following Immunization
Bibliographic record
Abstract
BACKGROUND: While adverse events following immunization (AEFI) are frequent, there are limited data on the safety of reimmunizing patients who had a prior AEFI. Our objective was to estimate the rate and severity of AEFI recurrences. METHODS: We analyzed data from the AEFI passive surveillance system in Quebec, Canada, that collects information on reimmunization of patients who had a prior AEFI. Patients with an initial AEFI reported to the surveillance system between 1998 and 2016 were included. Rate of AEFI recurrence was calculated as number of patients with recurrence/total number of patients reimmunized. RESULTS: Overall, 1350 patients were reimmunized, of which 59% were 2 years of age or younger. The AEFI recurred in 16% (215/1350) of patients, of whom 18% (42/215) rated the recurrence as more severe than the initial AEFI. Large local reactions extending beyond the nearest joint and lasting 4 days or more had the highest recurrence rate (67%, 6/9). Patients with hypotonic hyporesponsive episodes had the lowest rate of recurrence (2%, 1/50). Allergic-like events recurred in 12% (76/659) of patients, but none developed anaphylaxis. Of 33 patients with seizures following measles mumps rubella with/without varicella vaccine, none had a recurrence. Compared with patients with nonserious AEFIs, those with serious AEFIs were less often reimmunized (60% versus 80%; rate ratio: 0.8; 95% confidence interval: 0.66-0.86). CONCLUSIONS: Most patients with a history of mild or moderate AEFI can be safely reimmunized. Additional studies are needed in patients with serious AEFIs who are less likely to be reimmunized.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".