Menstrual Cycle Parameters Are Not Significantly Different After COVID-19 Vaccination
Bibliographic record
Abstract
Background: Some studies have suggested minor changes in the menstrual cycle after COVID-19 vaccination, but more detailed analyses of the menstrual cycle are needed to evaluate more specific changes in the menstrual cycle that are not affected by survey-based recall bias. Materials and Methods: Using a pretest–post-test quasi-experimental evaluation of menstrual cycle parameters before and after COVID-19 vaccination, we conducted an anonymous online survey of two groups of North American women who prospectively monitor their menstrual cycle parameters daily including bleeding patterns, urinary hormone levels using the ClearBlue Fertility Monitor, or cervical mucus observations. The primary outcome measures were cycle length, length of menses, menstrual volume, estimated day of ovulation (EDO), luteal phase length, and signs of ovulation. Perceived (subjective) menstrual cycle changes and stressors were also evaluated in this study as secondary outcome measures. Results: Of the 279 women who initiated the survey, 76 met the inclusion criteria and provided 588 cycles for analysis (227 pre-vaccine cycles, 145 vaccine cycles, 216 post-vaccine cycles). Although 22% of women subjectively identified changes in their menstrual cycle, there were no significant differences in menstrual cycle parameters (cycle length, length of menses, EOD, and luteal phase length) between the pre-vaccine, vaccine, and post-vaccine cycles. Conclusions: COVID-19 vaccines were not associated with significant changes in menstrual cycle parameters. Perceived changes by an individual woman must be compared with statistical changes to avoid confirmation bias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".