Summary of the National Advisory Committee on Immunization (NACI) Statement—Recommendation on Repeated Seasonal Influenza Vaccination
Bibliographic record
Abstract
Background: Influenza vaccination is recommended annually; however, some studies have raised questions regarding whether repeated influenza vaccine administration may have unintended negative consequences for seasonal protection. Methods: The National Advisory Committee on Immunization (NACI) Influenza Working Group undertook an overview of systematic reviews on the effects of repeated influenza vaccination on vaccine effectiveness, efficacy, and immunogenicity. A systematic assessment of programmatic factors was conducted according to established NACI methods. The NACI evidence-based process was used to critically appraise the available evidence and to review recommendations. Results: The evidence base consisted of four eligible systematic reviews/meta-analyses. Repeated vaccination, including the current season, was consistently more effective than no vaccination in the current season. The evidence showed no significant difference or predictable trend in vaccine efficacy or effectiveness between vaccinations in two consecutive seasons compared to vaccination in the current season only. Conclusion: Overall, NACI concluded that there is evidence to recommend annual influenza vaccination, irrespective of whether an individual received the seasonal influenza vaccine in previous seasons. It is neither currently feasible nor warranted to modify existing annual influenza vaccination programs to account for potential negative or positive interference. NACI continues to strongly recommend that seasonal influenza vaccine should be offered annually to everyone six months of age and older who does not have contraindications to the vaccine, irrespective of previous seasons' influenza vaccination status.
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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 | 0.031 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".