The association of human papillomavirus vaccine with premature ovarian failure: A systematic review
Bibliographic record
Abstract
Introduction: The most important factor initiating cervical cancer is human papilloma virus (HPV) infection; although the HPV vaccine can significantly prevent the infection in women, there is contradictory findings about its side effects such as premature ovarian failure (POF). The present systematic review was conducted with aim to determine the association between this vaccine and POF. Methods: This study was conducted by search in the reliable databases of PubMed, Web of Science, Google Scholar, Embase, and Scopus by two researchers independently based on the PICO guidelines and the use of Mesh terms including Papillomavirus vaccine, Human Papillomavirus Recombinant Vaccine Quadrivalent, Primary Ovarian Insufficiency and Premature Ovarian Failure. Newcastle - Ottawa checklist was used to evaluate the quality of articles. Results: After review and evaluation of the quality of 128 primary articles, finally 7 articles were included in the systematic review. According to the results of population-based studies, there was no significant relationship between the HPV vaccine and POF; but there are case series which support the association between the HPV vaccine and POF. They declared that the cause of this complication was the induction of autoimmune response by the vaccine and the side-effects of the adjuvant used in it. Conclusion: Considering that the HPV vaccine is currently the most effective approach in preventing cervical cancer, it seems more reasonable to use preventive benefits of this vaccine. Further studies emphasizing the possible side effects of the vaccine can minimize the challenges associated with this vaccine in the future.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".