Bibliographic record
Abstract
A resurgence of pertussis cases among immunized and unimmunized individuals raised questions about vaccine effectiveness and waning immunity. We performed a test-negative case-control study, utilizing Ontario administrative and laboratory data. We used crude and adjusted multivariable logistic regression models to estimate odds ratios (ORs) comparing the odds of cases being vaccinated against that of controls. Vaccine effectiveness was calculated as (1-OR)x100. There were 5,867 individuals available for analysis (486 test-positive cases and 5,381 test-negative controls). Vaccine effectiveness was 80% (95%CI, 71%-86%), 84% (95%CI, 77%-89%), 62% (95%CI, 42%-75%), and 41% (95%CI, 0-66%) at 15-364 days, 1-3 years, 4-7 years, and ≥8 years since last immunization, respectively. The odds of developing pertussis increased 27% (95%CI, 20%-34%) per year from last immunization. Acellular, compared to whole cell, vaccine priming was associated with a two-fold increased risk of pertussis. These results have important policy implications for optimizing pertussis control and to spur vaccine development.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".