Risk of Recurrence of Adverse Events Following Immunization: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Reimmunizing patients who had an adverse event following immunization (AEFI) is sometimes a challenge because there are limited data on the risk and severity of AEFI recurrence. OBJECTIVE: To summarize the literature on the risk of AEFI recurrence. DATA SOURCES: PubMed, Embase, and Cochrane library. STUDY SELECTION: We included articles in English or French published before September 30, 2016. Articles were selected if they estimated the risk of AEFI recurrence in at least 5 individuals. Studies with experimental vaccines were excluded. DATA EXTRACTION: Data on study design, setting, population, vaccines, and AEFI recurrence were extracted. RESULTS: = .02) LIMITATIONS: Many studies, included few patients, and those with severe AEFIs were often not reimmunized. CONCLUSIONS: Despite vaccines being administered to millions of people annually, there are few studies in which researchers evaluated AEFI recurrence. Published studies suggest that reimmunization is usually safe. However in these studies, severe cases were often not 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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".