The value of childhood combination vaccines: From beliefs to evidence
Bibliographic record
Abstract
Although vaccination is one of the most cost-effective health care interventions, under-vaccination and variation in coverage rates lower than policy targets is rising in developed countries, partly due to concerns about vaccination value and benefits. By merging various antigens into a single product, combination vaccines represent a valuable tool to mitigate the burden associated with the numerous injections needed to protect against vaccine preventable infectious diseases and increase coverage rate, possibly through various behavioral mechanisms which have yet to be fully explored. Beyond their cost-effectiveness in protecting against more diseases with fewer injections, combination vaccines also have several other benefits, for children, their parents/carers, as well as for the health system and the population as a whole. The objectives of this review are to identify and illustrate the value of combination vaccines for childhood immunization. Evidence was classified into 2 groups: benefits for society and benefits for public health and healthcare systems. This article also highlights the value of innovation and challenges of combination vaccine development as well as the need for an increased number of suppliers to mitigate the impact of any potential vaccine shortage. Increasing public confidence in vaccines and combination vaccines is also critical to fully exploit their benefits.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".