Abstract 23: Lower Heart Rate Variability Associated With Incident Coronary Heart Disease and Death in Post-Menopausal Women
Bibliographic record
Abstract
Rationale: Lower heart rate variability (HRV) has been associated with increased all-cause mortality and adverse cardiovascular events including sudden death. The role of HRV in post-menopausal women is understudied. Therefore, we tested the hypothesis of a significant association between HRV and incident coronary heart disease (CHD) and CHD death in post-menopausal women. Methods: This is an analysis of prospective data collected during the Women’s Health Initiative (WHI) clinical trials. A total of 57,061 post-menopausal women were included. HRV was quantified using two variables; standard deviation of all normal-to-normal RR intervals (SDNN, milliseconds), and the root mean square of successive differences in normal-to-normal RR intervals (RMSSD, milliseconds). We used Cox proportional hazards models to test the association between HRV and incident CHD, hard CHD (including myocardial infarction and fatal CHD), and total CHD death. We controlled for age, race, alcohol use, physical activity, depression, BMI, smoking, diabetes, hypertension, lipid disorders, and family history of CHD. Results: The results are as highlighted for incident and hard CHD in table 1 and for CHD death in table 2. There were statistically significant associations between lower HRV (as a continuous variable and as quartiles) and higher levels of incident CHD. Conclusion: In post-menopausal women, lower HRV was associated with a modestly higher, but statistically significant, risk for incident cardiovascular events, including fatal and non-fatal myocardial infarction. Factors associated with lower HRV and cardiac autonomic impairment may be candidates for reducing CHD risk, such as better glycemic control and improved physical activity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".