Determinants of poor chronic obstructive pulmonary disease control
Bibliographic record
Abstract
Uncontrolled COPD has been associated with reduced health-related quality of life, activity impairment, and increased use of healthcare resources. However, limited research is available on the factors associated with poor disease control in COPD patients. This study aimed to explore the factors associated with poor disease control in patients with COPD. The current cross-sectional study was conducted on patients with COPD who attended outpatient respiratory clinics at two major hospitals in Jordan. Information about disease and medication-related characteristics was collected through patient interviews and medical files. Validated instruments, including the 4-item medication adherence scale and the hospital anxiety and depression scales, were used to assess medication adherence, anxiety, and depression among the study participants. COPD severity was assessed using the GOLD classification criteria. Ordinal regression analysis was conducted to explore the variables associated with poor COPD control. In total, 702 patients participated in the study, with a median (interquartile range) age of 68 years (58-77). According to the GOLD report, most of the participants were in the B group (low risk/high symptoms; 40.2%), followed by the D group (high risk/high symptoms; 28.2%). Older age, higher depression scores, and a higher number of prescribed medications were associated with poorer COPD control, while not receiving LAMA (long-acting muscarinic antagonists) was associated with better control. Future mental health care initiatives should address the prevalence of depression symptoms in COPD patients and manage them effectively to improve COPD control and prevent further complications, with special attention to older patients, those receiving multiple medications, and those using LAMA.
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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 | 0.005 |
| 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) | 0.002 | 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".