Correlates of resting and exertional dyspnea among older adults with obstructive lung disease: a cross-sectional analysis of the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Introduction In patients with chronic obstructive pulmonary disease (COPD), the perception of dyspnea is related to quality of life, and is a better predictor of mortality than the severity of airway obstruction. The purpose of the current study was to use population-level data from the Canadian Longitudinal Study on Aging (CLSA) to identify potential correlates of dyspnea in adults with obstructive lung disease. Methods Data from participants with a self-reported obstructive lung disease (asthma or COPD) were used for analysis (n=2,854). Four outcome variables were assessed: self-reported dyspnea at 1) rest, 2) walking on a flat surface, 3) walking uphill/climbing stairs, 4) following strenuous activity. Potential sociodemographic, health, and health behaviour correlates were entered in to logistic regression models. Results Higher body fat percentage, and worse forced expiratory volume in one second were associated with higher odds of reporting dyspnea. Females with an anxiety disorder (OR=1.91, CI: 1.29, 2.83) and males with a mood disorder (OR=2.67, CI: 1.53, 4.68) reported higher odds of experiencing dyspnea walking on a flat surface, independent of lung function and other correlates. Dyspnea while walking uphill/climbing stairs was associated with a slower timed up and go time in females (e.g. OR=1.18, CI: 1.10) and males (OR=1.19, CI: 1.09, 1.30). Conclusions In addition to traditional predictors such as lung function and body composition, we found that anxiety and mood disorders, as well as functional fitness were correlates of dyspnea. Further research is needed to understand whether targeting these correlates leads to improvements in perceptions of dyspnea.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".