Ovulatory and anovulatory cycle phase influences on QT interval dynamics during the menstrual cycle
Bibliographic record
Abstract
BACKGROUND: Ovarian hormones affect cardiovascular health yet few sufficient-sized studies with reliable ovulatory documentation have assessed the QTc-hormonal relationship. This study investigated QTc changes across ovulatory and anovulatory menstrual cycle phases. METHODS: This prospective cohort investigation, a cardiac sub-study of the Menstruation and Ovulation Study 2 (MOS2), involved 62 healthy, regularly menstruating community-dwelling women during spontaneous menstrual cycles. Electrocardiographic recordings were obtained within-woman during different cycle phases: mid-follicular for all, and luteal (ovulatory) or premenstrual (anovulatory), documented by the validated Quantitative Basal Temperature© method. Fridericia's formula rate-corrected the QT interval (QTc). A subsequent meta-analysis was conducted, pooling data from three additional studies to evaluate ovulatory follicular-luteal phase QTc changes. RESULTS: In the 26 ovulatory cycles, QTc minimally decreased from the mid-follicular to the luteal phases (383.0 ± 12.8 vs 382.6 ± 12.8 msec, P = .859). QTc in the 36 anovulatory cycles tended to increase from mid-follicular to premenstrual phases (381.7 ± 13.1 vs 385.0 ± 16.1 msec, P = .166). The meta-analysis in ovulatory cycles yielded a random-effects weighted mean QTc shortening of 1.67 msec (P = .53) in the luteal vs the follicular phase, aligning with our cohort data. CONCLUSION: In confirmed ovulatory cycles, QTc changes were minimal, showing no meaningful luteal phase QTc shortening. QTc changes in anovulatory cycles were also insignificant, with a small QTc prolongation likely due to longer estradiol exposure not counterbalanced by progesterone. Under normal physiological conditions, QTc changes during the menstrual cycle are trivial, and menstrual status does not need to be considered when interpreting the QT interval.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".