Original Contribution Underestimating the Safety Benefits of a New Vaccine: The Impact of Acellular Pertussis Vaccine Versus Whole-Cell Pertussis Vaccine on Health Services Utilization
Bibliographic record
Abstract
The population-level safety benefits of the acellular pertussis vaccine may have been underestimated because only specific adverse events were considered, not overall impact on health services utilization. Using the Vaccine and Immunization Surveillance in Ontario (VISION) system, the authors analyzed data on 567,378 children born between April 1994 and March 1996 (before introduction of acellular pertussis vaccine) and between April 1998 and March 2000 (after introduction of acellular pertussis vaccine) in Ontario, Canada. Using the self-controlled case series study design, they examined emergency room visits and hospital admissions occurring after routine pediatric vaccinations. The authors determined the relative incidence of events taking place before introduction of the acellular vaccine versus after introduction by calculating relative incidence ratios (RIRs). The observed RIRs demonstrated a highly statistically significant reduction in relative incidence after introduction of the acellular vaccine. RIRs for vaccine administered at ages 2, 4, 6, and 18 months were 1.82 (95 % confidence interval (CI): 1.64, 2.01), 1.91 (95 % CI: 1.71, 2.13), 1.54 (95 % CI: 1.38, 1.72), and 1.51 (95% CI: 1.34, 1.69), respectively, comparing event rates before the introduction of acellular vaccine with those after introduction. The authors estimated that approximately 90 emergency room visits and 9 admissions per month were avoided by switching to the acellular vaccine, which is a 38-fold higher impact than when they considered only admissions for febrile and afebrile convulsions. Future analyses comparing vaccines for safety should
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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 | 0.012 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".