A pilot study of heart rate variability and apneic-hypopneic events in non-obese women with polycystic ovary syndrome during sleep.
Bibliographic record
Abstract
Background: Patients with polycystic ovary syndrome (PCOS) have a higher apnea-hypopnea index (AHI) than did controls. The relationship between heart rate variability (HRV) changes and apneic-hypopneic events in non-obese women with PCOS during sleep is yet to be determined. Methods: All participants (14 untreated women with PCOS and 11 age-and BMI-controlled healthy women, with mean body mass indexes of 21.51±0.63 and 21.27±0.66 Kg/cm^2, respectively) underwent whole-night standard polysomnographic (PSG) monitoring and assessment of serum hormone and homeostasis model assessment of insulin resistance (HOMA-IR). Short-term HRV (in different sleep stages) and long-term HRV (6-hour sleep) were obtained by a power spectral analys is. Results: The AHI and arousal index during the non-rapid eye movement stage (AHI(subscript NREM) and ARI(subscript NREM) were both higher in non-obese women with PCOS than those in the control group (0.032±0.028 vs. 0.698±0.243 p=0.004; 11.45±0.864 vs. 8.636±0.847 p=0.045 Triangular interpolation of the NN interval histogram TINN) of long-term HRV in the PCOS group was also lower (303.9±19.23 vs. 355.9±10.97, p=0.0484) .TINN in all subjects was negatively correlated to the AHI, after adjusting for age, body fat percentage, and serum androgens. TINN had negative relation with highly sensitive C-reactive protein (hsCRP) while AHIREM was positively related to hsCRP. Conclusions: Non-obese patients with PCOS showed poorer long-term TINN during sleep compared to the controls. The TINN was related to AHI and hsCRP. PCOS patients suffered from worsened cardiac nerve autonomous function that could lead to long-term cardiovascular risk during sleep even when they are not obese.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".