Two-dose varicella vaccine effectiveness in China: a meta-analysis and evidence quality assessment
Bibliographic record
Abstract
Abstract Background The objectives of this review were to evaluate the vaccine effectiveness (VE) of the two-dose varicella vaccine for healthy children in China and explore the application of the approach of Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) in observational studies on VE. Methods We searched for observational studies on two-dose varicella VE for children in China aged 1–12 years that were published from 1997 to 2019, and assessed the quality of each study using the Newcastle Ottawa Scale (NOS). We used meta-analysis models to obtain the pooled two-dose VE, and the studies were divided into subgroups and analysed according to whether or not it was an outbreak investigation and its NOS score. The quality of evidence of VEs were rated by approach of the GRADE system. Results A total of 12 studies and 87,196 individuals were included. The pooled two-dose VE was 90% (95% confidence interval [CI]: 69–97%). The VE of outbreak studies (87% [95% CI: 76–93%]) was lower than non-outbreak studies (99% [95% CI: 98–99%]). There was no significant difference in VEs by different NOS quality. The quality of the evidence assessment of pooled two-dose VE was “low”, which was rated down by one category in limitations and publication bias respectively and rated up by two category in large effect. The quality of evidence assessment in subgroup of NOS score ≥ 7 was “moderate”. Conclusions The VE of two-dose varicella vaccine is relatively high in preventing varicella, and is recommended for countries which need further control for varicella. However, higher quality evidence is needed as a supplement for stronger recommendations. The approach of GRADE could be applied for rating the quality of evidence in observational study.
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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 | 0.057 | 0.115 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.041 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".