Primary Versus Secondary Failure After Varicella Vaccination
Bibliographic record
Abstract
BACKGROUND: Two-dose varicella vaccination is recommended for optimal control of varicella in populations with high (>90%) 1-dose coverage. Optimal timing of the second dose may depend on whether breakthrough varicella results from primary vaccine failure (no protective immunity after vaccination) or secondary vaccine failure (waning protective immunity). METHODS: Published literature (1995 to 2012) on vaccine failure after varicella vaccination cited in PubMed and other online sources was reviewed. RESULTS: Nineteen publications detailed 21 varicella outbreaks with breakthrough varicella rates ranging from 0% to 42%; the publications showed no consistent trend between breakthrough varicella rate and time since vaccination. CONCLUSIONS: Literature to date indicates a relatively high rate of primary vaccine failure and limited evidence of secondary vaccine failure among 1-dose varicella vaccine recipients, suggesting that a short interval between 2 doses might be preferable in countries considering implementation of universal varicella vaccination to reduce breakthrough varicella. However, any potential disruption to well-established vaccination schedules should be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".