The Effect of COVID-19 Vaccination on Menstrual Cycles of Adolescents and Young Adults: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background and Study Objective The binding of the SARS-CoV-2 spike protein from vaccination can affect the menstrual cycle. Most studies have focused on women of reproductive age, with less attention given to adolescent and young women, despite their increased risk of heightened responses to vaccines. Thus, we consolidated evidence on menstrual changes after COVID-19 vaccination for adolescent and young women. Methods OVID MEDLINE, EMBASE and CINAHL databases were searched (January 2020-December 2024) for peer-reviewed studies on COVID-19 vaccination on menstruating people <25. Of 80 articles identified, 15 met the inclusion criteria after review by two independent reviewers. We estimated risk ratios (RR) and mean differences (MD) when data permitted. We assessed publication bias with funnel plots and evaluated heterogeneity using Cochran's Q, Galbraith plots, and I² statistic. Outcomes included any measured or perceived changes in menstrual cycles, in bleeding length, and in cycle length (i.e. length between the first day of bleeding of two periods). Results Among the 15 studies, 24,647 adolescents and young adults aged 12 to 25 were included. The summary effect measure showed no effect of vaccination on any menstrual change (RR:1.09; 95% CI: 0.84-1.42) and significant heterogeneity across studies (I 2 = 69%). There was, however, a greater risk of longer cycle length after vaccination (RR=1.17, 95% CI: 1.08, 1.27) and no heterogeneity between these two studies (I 2 =0%); however, when assessed continuously there was a non-significant increase in cycle length (MD=0.24 days, 95% CI: -0.34, 0.82). No studies assessed menses bleeding length in adolescent and young women. Funnel plots suggested no publication bias. Conclusion Although few studies were included, available data suggest that there may be increased risk of a longer menstrual cycle length in adolescents and young women, but no other changes were identified. Further data are needed.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".