Dyspnea as clinical indicator in patients with chronic obstructive pulmonary disease
Bibliographic record
Abstract
The aim of this study was to evaluate the relationship between dyspnea and functional, psychosocial and quality of life parameters among persons with chronic obstructive pulmonary disease (COPD). We conducted a cross-sectional study of 90 stable COPD patients recruited from a specialized respiratory clinic. Dyspnea was measured using the ATS-DLD-78 questionnaire modified dyspnea scale (1-5 scale). Physical and functional evaluation included spirometry and six minute walking tests. Subjects then completed five psychological questionnaires: the Coping Inventory for Stressful Situations, the State/Trait Anxiety Inventory, the Beck Depression Index, the NEO-Five Factor Personality Inventory, and the Interpersonal Relationships Inventory. Patients also completed two disease-specific health-related quality of life (HRQL) questionnaires: St. George's Respiratory Questionnaire (SGRQ) and Chronic Respiratory Questionnaire (CRQ). Subjects were predominantly male (n = 65) with a mean age of 68 years (+/- standard deviation 7.6). Over half (54%) the patients reported severe dyspnea (grade 5), and a quarter (24%) reported moderate dyspnea (grade 3-4). Mean FEV1 was 37.8 +/- 14.8% predicted. The mean total SGRQ score was 49 +/- 16 and the CRQ total score was 4.2 +/- 0.9. Dyspnea severity was associated with poorer HRQL scores and decreased physical performance. Based on linear regression, dyspnea scores--but not spirometric values--also correlated with indices of anxiety, depression, and neuroticism. Dyspnea correlated more strongly with HRQL and with indices of anxiety and depression than spirometric values. Although spirometry is often used to evaluate disease severity, dyspnea which is a patient centered outcome better reflect overall disease impact among COPD patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".