[Risk factors of obstructive sleep apnea hypopnea syndrome and its treatment in female patients].
Bibliographic record
Abstract
OBJECTIVE: To identify risk factors of obstructive sleep apnea hypopnea syndrome (OSAHS) in female patients, and assess its treatment and health-related quality of life (HRQoL) of the patients. METHODS: One hundred and ninety three female patients undergoing polysomnography whith sleep breath disorders were recruited and divided into non-OSAHS group and OSAHS group. Age, body mass index (BMI), and prevalence of menopause and hypertension were compared between the two groups. The associations of those variables with apnea hypopnea index (AHI) and lowest pulse oxygen saturation (LSpO2) were analysed. The treatment of OSAHS and its impact on HRQoL assessed by the Calgary quality of life index (SAQLI) were evaluated. RESULTS: The OSAHS group had greater mean age, BMI, and prevalence of menopause and hypertension than the non-OSAHS group (P<0.05). Those variables were significantly correlated with AHI and LSpO2 (P<0.05). Weight control and positional therapy were the most common treatment for sleep breath disorders. Weight loss and continuous positive airway pressure improved the Epworth sleep scale (ESS) of the patients with OSAHS significantly (P<0.05). But positional therapy made no difference (P>0.05). The ESS of the 32.5% of patients who did not undergo any treatment was worsened during the same period of time (P<0.05). No differences were found in the scores of the four domains of SAQLI between the two groups (P>0.05). The stepwise multiple regression analysis identified Pittsburgh sleep quality index (PSQI), ESS and AHI as independent predictors for the total score of SAQLI (P<0.05). CONCLUSION: Older age, greater BMI, menopause and hypertension are risk factors of OSAHS in female patients. OSAHS was not well managed in female patients. PSQI, ESS and AHI can be used as predictors for HRQoL.
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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 | 0.001 |
| 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) | 0.003 | 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".