Effectiveness of Pneumococcal Conjugate Vaccine Using a 2+1 Infant Schedule in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: In the province of Quebec, Canada, pneumococcal conjugate vaccine (PCV) is offered to all children aged less than 5 years, and a 2+1 schedule (2, 4, and 12 months) is recommended for low-risk infants, with other schedules including a lower number of doses for older children. OBJECTIVE: To estimate PCV effectiveness against invasive pneumococcal disease (IPD). METHODS: IPD cases in children aged 2-59 months and reported during the years 2005-2007 were eligible and uninfected controls were randomly identified in the provincial health insurance registry. Parents were interviewed by telephone and immunization records were reviewed. The PCV effectiveness was computed using unconditional logistic regression models adjusting for potential confounders. RESULTS: 180 IPD cases (60.4% of total reported) and 897 controls were included. Predictors of IPD risk were age, season, high-risk medical conditions, day-care attendance, and low family income. Overall PCV protection (> or =1 dose) against IPD caused by any serotype was 60% (95% CI: 38%-75%), and was 92% (83%-96%) against IPD caused by vaccine serotypes. Among low-risk children who received the recommended 2+1 schedule, 6 cases of vaccine failure occurred after the first dose, 1 case after the second dose, and no cases after the booster dose. CONCLUSION: These results confirm the effectiveness of PCV after 2 and 3 doses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".