Spontaneous preterm birth risk among <scp>HPV</scp>‐vaccinated and ‐unvaccinated women: a nationwide retrospective cohort study of over 240 000 singleton births
Bibliographic record
Abstract
OBJECTIVE: To determine whether prior human papillomavirus (HPV) vaccination contributes to preterm birth risk. DESIGN: Population-based retrospective cohort study. SETTING: Denmark. POPULATION: A cohort of 243 136 primiparous females born in the period 1961-2004 who had a singleton delivery at >22 weeks of gestation occurring from October 2006 to December 2018. METHODS: High-quality nationwide registries were linked to provide information on demographics, birth outcomes, HPV vaccination status, smoking, body mass index (BMI), and cervical lesions and treatment history. MAIN OUTCOME MEASURES: We assessed the association between HPV vaccination status and spontaneous preterm birth using logistic regression. To address age at vaccination, we performed a stratified analysis by vaccination before and after 17 years of age. RESULTS: In age-adjusted and fully adjusted models, there was a nonsignificant difference in the odds of spontaneous preterm birth between vaccinated and unvaccinated women (OR 1.05 (95% CI 0.99-1.12) and OR 1.04 (95% CI 0.98-1.10), respectively). There was no difference in the odds of spontaneous preterm birth in relation to time between vaccination and pregnancy. In contrast, compared with unvaccinated women, the odds of preterm birth were lower among women vaccinated before the age of 17 years (fully adjusted OR 0.87, 95% CI 0.75-1.00). This association was not present for women vaccinated at ≥17 years of age. CONCLUSIONS: In this large, population-based cohort, we found reduced odds of spontaneous preterm birth among women vaccinated against HPV at an early age compared with women who were unvaccinated. It seems conceivable that HPV vaccination may not only reduce the incidence of cervical cancer and severe precursors, but also reduce the risk of preterm birth related to HPV infection.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".