Relationships Between Health-Related Quality of Life and Social Support in Patients with Obstructive Sleep Apnea-Hypopnea Syndrome
Bibliographic record
Abstract
Objective To study the relationships among health-related quality of life(HRQL),social support,excessive daytime sleepiness(EDS) and PSG parameters in patients with obstructive sleep apnea-hypopnea syndrome(OSAHS).Methods Eighty-five patients were recruited who were diagnosed as OSAHS by overnight polysomnography from August 2007 through November 2007 in West China Hospital.The Calgary sleep apnea quality of life index(SAQLI) was used for HRQL,social support rating scale(SSRS) was used for social support,and Epworth sleepiness scale(ESS) was used for EDS.The Pearson linear correlation and stepwise multiple regression analysis were used to analyze the correlation among SAQLI,SSRS,ESS,and PSG.Results The SAQLI was correlated with SSRS score(r=0.402,P0.01);ESS score(r=-0.505,P0.01);apnea-hypopnea index(AHI)(r=-0.269,P0.05) and lowest artery oxygen saturation(LSaO2)(r=0.226,P0.05).Stepwise multiple regression analysis determined two variables,the SSRS and ESS score,as independent factors for predicting the total score of SAQLI which accounted for 37.3% of the total variance in the total score on SAQLI(R2=0.373,P0.001).Conclusions The HRQL of patients with OSAHS was correlated with the SSRS score,ESS score and PSG parameters.The former two were the more important factors to affect the HRQL of patients with OSAHS.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".