Factors associated with anxiety and depression among patients with chronic obstructive pulmonary disease
Bibliographic record
Abstract
OBJECTIVES: This study investigated factors associated with anxiety and depression in COPD outpatients. METHODS: A cross-sectional study of 702 COPD outpatients from two major Jordanian hospitals using the Hospital Anxiety and Depression Scale (HADS) was conducted. RESULTS: Significant associations were found with gender (Anxiety OR: 5.29, 95%CI: 2.38-11.74; Depression OR: 0.20, 95%CI: 0.08-0.51), disease severity (Anxiety OR: 2.97, 95%CI: 1.80-4.91; Depression OR: 15.95, 95%CI: 5.32-52.63), LABA use (Anxiety OR: 16.12, 95%CI: 8.26-32.26; Depression OR: 16.95, 95%CI: 8.33-34.48), medication count (Anxiety OR: 0.73, 95%CI: 0.59-0.90; Depression OR: 0.51, 95%CI: 0.40-0.64), mMRC score (Anxiety OR: 2.41, 95%CI: 1.81-3.22; Depression OR: 2.31, 95%CI: 1.76-3.03), and inhalation technique (Anxiety OR: 0.95, 95%CI: 0.93-0.97; Depression OR: 0.92, 95%CI: 0.90-0.95). Other factors associated with anxiety included high income, urban living, diabetes, hypertension, LAMA use, and fewer COPD medications. Depression was also linked with heart disease, increased age, and longer disease duration. CONCLUSION: The prevalence of anxiety and depression among COPD patients necessitates targeted interventions. Future research that recruits a more diverse sample in multiple sites and establishes the cause-effect relationship between the study predictors and outcome could provide a more robust conclusion on factors associated with anxiety and depression among COPD patients.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".