Human Papillomavirus Vaccination and Premature Ovarian Failure: A Disproportionality Analysis Using the Vaccine Adverse Event Reporting System
Bibliographic record
Abstract
INTRODUCTION: There have been public health concerns about a potential association between human papillomavirus (HPV) vaccines and premature ovarian failure (POF) in young women. OBJECTIVE: To identify a potential safety signal of POF after HPV vaccination using the United States (US) Vaccine Adverse Event Reporting System (VAERS) database. METHODS: We manually selected relevant MedDRA preferred terms related to POF and identified in VAERS all POF reports in women less than 40 years of age between 2 July 1990 and 14 May 2018, followed by a review of narratives to confirm the cases. We conducted descriptive analyses on age, POF type, HPV vaccine type (HPV2, HPV4, HPV9), time to onset of POF, and dose rank. We described trends in reporting over time and assessed a potential safety signal using the proportional reporting ratio (PRR). RESULTS: Of the 228,341 eligible POF reports, 281 (0.1%) were suspected to be associated with HPV vaccines. Median patient age was 15 years (range 11-39 years). POF events consisted mainly of amenorrhea (80.4%) and premature menopause (15.3%). Mean number of reported POF events significantly increased after the first HPV vaccine launch in 2006 with 22.2 POF cases/year up from 1.4 POF cases/year before the launch. PRR was 46.1 (95% confidence interval: 31.7-67.2) and sensitivity analyses yielded similar estimates. CONCLUSION: Our study suggests the presence of a potential safety signal of POF associated with HPV vaccination, which may only be partly attributed to notoriety bias. Due to the well-known limitations of spontaneous reporting data, further investigations are warranted.
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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 | 0.040 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".