Effectiveness of the Quadrivalent Human Papillomavirus Vaccine Against Cervical Dysplasia in Manitoba, Canada
Bibliographic record
Abstract
PURPOSE: Effectiveness of the quadrivalent human papillomavirus (QHPV) vaccine against cervical dysplasia has not been estimated using population-based individual level data. We assessed the vaccine effectiveness (VE) of the QHPV vaccine against cervical dysplasia using data collected routinely in Manitoba. METHODS: Females ≥ 15 years old who received the QHPV vaccine in Manitoba between September 2006 and April 2010 privately (n = 3,541) were matched on age to up to three nonvaccinated females (n = 9,594). We used Cox regression models to estimate the hazard ratios for three outcomes: atypical squamous cells of undetermined significance (ASCUS), low-grade squamous intraepithelial lesions (LSILs), and high-grade SILs (HSILs). RESULTS: Among the 15- to 17-year-olds, the adjusted VE estimates were 35% (95% CI, -19% to 65%), 21% (-10% to 43%), and -1% (-44% to 29%) against the detection of HSILs, LSILs, and ASCUS, respectively. The corresponding estimates were higher (46% [0% to 71%], 35% [10% to 54%], and 23% [-8% to 45%]) among those who had ≥ one Pap smear after enrollment. The QHPV vaccine was associated with 23% (-17% to 48%) reduction in HSIL risk among those ≥ 18 with no history of abnormal cytology, but there was no evidence of protection among those with such a history (-8% [-59% to 27%]). CONCLUSION: A significant percentage of vaccinated women may not be protected against HSIL and lesser dysplasia especially if they were vaccinated at older age (≥ 18) or had abnormal cytology before vaccination. These findings affirm the importance of vaccination before any significant exposure to HPV occurs and underscore the need for screening programs that cover all sexually active women, even if they were vaccinated.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".