Prevalence of menstrual alterations following COVID-19 vaccination: systematic review & meta-analysis
Bibliographic record
Abstract
COVID-19 vaccines can lead to diverse local and systemic side effects, but there is limited evidence concerning their association with menstrual cycle changes. This study aimed to assess the prevalence of menstrual cycle alterations after COVID-19 vaccination among adult women. We systematically searched the PubMed, Web of Science and Science Direct databases for observational studies that included adult women and investigated the range of menstrual alterations. The quality of the studies was evaluated via the Newcastle–Ottawa scale. All the data were analyzed via Comprehensive Meta-Analysis Software Version 4.0. Forest plots were created to calculate the individual and pooled prevalence rates of different types of menstrual changes and 95% confidence intervals (CI) via fixed-effects and random-effects models, as appropriate. Heterogeneity was assessed with Q statistics and the I 2 test. Eleven studies, encompassing 26,283 adult women, met our eligibility criteria. Among the selected studies, five were cohort studies, five were cross-sectional studies, and one employed a case‒control design. The menstrual changes included abnormal cycle duration, dysmenorrhea, irregular cycles, and abnormal cycle flow (heavy and light flow), with pooled percentages of 27.3% (CI: 7.2–64.6%), 22% (CI: 5.2–59.4%), 16% (CI: 5.8–37.2%), 11.7% (CI: 5.8–22%), and 5.5% (CI: 2.3–12.5%), respectively. This review highlights the prevalence of menstrual changes after COVID-19 vaccination and emphasizes the importance of considering menstrual health as an integral part of postvaccination monitoring and health care interventions. However, longitudinal studies are essential for establishing a definitive causal relationship between COVID-19 vaccination and menstrual alterations.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.038 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".