Quality of life associated with breathlessness in the multinational Burden of Obstructive Lung Disease (BOLD) study: A cross-sectional analysis
Bibliographic record
Abstract
Introduction Evidence of an association between breathlessness and quality of life from population-based studies is limited. We aimed to investigate the association of both physical and mental quality of life with breathlessness across several low-, middle- and high-income countries.Methods We analysed data from 19 714 adults (31 sites, 25 countries) from the Burden of Obstructive Lung Disease (BOLD) study. We measured both mental and physical quality of life components using the SF-12 questionnaire, and defined breathlessness as grade ≥2 on the modified Medical Research Council scale. We used multivariable linear regression to assess the association of each quality-of-life component with breathlessness. We pooled site-specific estimates using random-effects meta-analysis.Results Both physical and mental component scores were lower in participants with breathlessness compared to those without. This association was stronger for the physical component (coefficient = −7.59; 95%CI −8.60, −6.58; I2 = 78.5%) than for the mental component (coefficient = −3.50; 95%CI −4.36, −2.63; I2 = 71.4%). The association between physical component and breathlessness was stronger in high-income countries (coefficient = −8.82; 95%CI −10.15, −7.50). Heterogeneity across sites was partly explained by sex and tobacco smoking.Conclusion Quality of life is worse in people with breathlessness, but this association varies widely across the world.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.011 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".