A universal infant rotavirus vaccine program in two delivery models: Effectiveness and adverse events following immunization
Bibliographic record
Abstract
Rotavirus is the most common cause of diarrhea leading to hospitalization in young children. Rotavirus vaccines are available in Canada but have not been introduced in all provinces. In a controlled trial, 2 study sites (Prince Edward Island and the Capital District Health Authority (District 9, Nova Scotia) introduced universal rotavirus vaccine programs for infants at 2 and 4 months of age beginning 1 December 2010, using public health nurse or general practitioner-delivery models, respectively. A third site (Saint John, NB) served as the non-intervention control setting. Vaccine coverage, rotavirus hospitalizations, intussusception and all-cause diarrhea were monitored. A universal rotavirus vaccine program with >90% coverage was associated with reductions in rotavirus-associated hospitalizations (from a peak of 52.8 hospitalizations/100,000 population to 0 hospitalizations) in infants < 12 months and 1 to < 2 y of age 12 months after program implementation. No apparent reduction occurred in the site with vaccine coverage of < 40%, or in the non-intervention control site. No cases of intussusception were associated with vaccine receipt, and no increase in all-cause diarrhea was observed. A universal infant rotavirus vaccine program with high coverage was associated with reductions in rotavirus and no safety signals; no reduction was observed in settings with low vaccine coverage.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".