The Impact of the HPV Vaccine on Preterm Birth in British Columbia
Bibliographic record
Abstract
OBJECTIVES: Recent evidence has suggested that human papillomavirus (HPV) vaccination may reduce the risk of preterm birth. The objective of this study was to determine the feasibility of linking existing provincial databases to begin to understand whether the risk of preterm birth is lower in HPV-vaccinated women in British Columbia (BC). METHODS: In this population-based retrospective cohort study of women delivering infants in BC, data on birth outcomes and HPV vaccination status from the BC Perinatal Data Registry and the Panorama Public Health Information System were linked. We compared the overall and spontaneous preterm birth rates between vaccinated and unvaccinated women using logistic regression. RESULTS: Among women who were age-eligible for HPV vaccination in school-based programs, there were 5447 deliveries from 5399 individuals between 2015 and 2018. Of these, 2925 (54.2%) women had been vaccinated in the school-based program. Overall and spontaneous preterm birth were significantly associated with previous preterm delivery and maternal substance use, but were not found to be associated with HPV vaccination status. CONCLUSIONS: We were readily able to link provincial databases to assess the role of HPV vaccination in preterm birth risk. These pilot data did not show a significant association between HPV vaccination status and preterm birth. Subsequent larger studies are warranted to better assess the presence of a relationship, which may promote vaccination and result in improved reproductive outcomes for women and their infants.
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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".