Health-related quality of life and its associated factors in patients with chronic obstructive pulmonary disease
Bibliographic record
Abstract
OBJECTIVE: The present study aimed to evaluate HRQOL and to explore the factors associated with poor HRQOL among patients with COPD. METHODS: In the present cross-sectional study, the validated St George's Respiratory Questionnaire for COPD patients (SGRQ-C) was used to evaluate HRQOL among 702 patients with COPD at two major hospitals in Jordan in the period between January and April 2022. Quantile regression analysis was used to explore the factors associated with HRQOL among the study participants. RESULTS: According to SGRQ-C, the HRQOL of the study participants was greatly impaired with a total SGRQ of 55.2 (34-67.8). The highest impairment in the HRQOL was in the impact domain with a median of 58.7 (29-76.3). Increased number of prescribed medications (β = 1.157, P<0.01), older age (β = 0.487, P<0.001), male gender (β = 5.364, P<0.01), low education level (β = 9.313, P<0.001), low and moderate average income (β = 6.440, P<0.05, and β = 6.997, P<0.01, respectively) were associated with poorer HRQOL. On the other hand, being married (β = -17.122, P<0.001), living in rural area (β = -6.994, P<0.01), non-use of steroids inhalers (β = -3.859, P<0.05), not receiving long acting muscarinic antagonists (LAMA) (β = -9.269, P<0.001), not receiving LABA (β = -8.243, P<0.001) and being adherent to the prescribed medications (β = -6.016, P<0.001) were associated with improved HRQOL. Furthermore, lower disease severity (stage A, B, and C) (β = -23.252, -10.389, and -9.696 respectively, P<0.001), and the absence of comorbidities (β = -14.303, P<0.001) were associated with better HRQOL. CONCLUSIONS: In order to maximize HRQOL in patients with COPD, future COPD management interventions should adopt a multidisciplinary approach involving different healthcare providers, which aims to provide patient-centered care, implement personalized interventions, and improve medication adherence, particularly for patients who are elderly, males, have low socioeconomic status, receive multiple medications and have multiple comorbid diseases.
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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 | 0.001 |
| 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".