Evidence of hot flash–induced awakenings in menopausal women
Bibliographic record
Abstract
Abstract Background Women are disproportionately afflicted with dementias, though the exact risk factors explaining why are yet to be determined. Additionally, perimenopausal women experience deficits in verbal memory during menopause compared to their pre‐ and postmenopausal state. Because sleep debt also leads to verbal memory deficits, we explored whether one of the hallmark symptoms of menopause, hot flushes, cause an increase of sleep disturbances. Methods N = 63 perimenopausal and postmenopausal women (mean age of 53.1 ± 1.1 years old) with hot flushes were recorded using actigraphy for sleep/wake activity and skin conductance for objective hot flushes over one night. The two time series were compared to each other using Granger causality to establish statistical causality. The number of objective hot flushes, determined by skin conductance, and amount of time spent awake were compared with linear regression. Results Of the 63 women, 76.2% were found to have skin conductance which granger‐caused more awake activity at night. Additionally, we have reinforced that a greater number of nightly objective hot flushes is associated with greater time spent awake (b = 28 minutes/hot flush, p < .001). Conclusion Those women who have hot flush granger‐caused awakenings may be at a higher risk of sleep disturbances during and following menopause. The relationship between hot flushes and lower sleep quality has previously been shown, but here we show that there may be a causal link of hot flushes causing night‐time awakenings. These hot flush granger‐caused awakenings may represent a female‐specific risk factor for cognitive impairment.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".