How to better inform the decision making about universal influenza vaccination in children
Bibliographic record
Abstract
The disease burden of seasonal influenza in young children is substantial. And yet only the USA, Canada, Finland and one state in Australia currently have a routine influenza vaccine policy in place for young children. Few countries seem keen to follow their lead. This paper reviews the evidence required to inform a policy of universal paediatric vaccination; key features include protective effect, economic impacts and the safety of influenza vaccination in this age group. We found that i. there is insufficient data on the protective effect of vaccinating infants aged 6-23 months, ii. there are very few economic evaluations and most of the current published economic evaluations involve modelling and were performed using data from a variety of sources which are not setting specific, and iii. safety data have not been specifically addressed by an in-depth separate systematic review. To better inform relevant policy making, we suggest that interdisciplinary research, (combining epidemiology and health economics at least), is required to fully examine the protective effect, economic impacts and safety of influenza vaccination in children aged 6-59 months. We also suggest that the safety data on influenza vaccination in this age group should be assessed specifically by an in-depth separate systematic review, using published and grey literature.
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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 | 0.100 | 0.411 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".