Shortness of breath associated with chronic conditions among those with and without asthma or COPD
Bibliographic record
Abstract
Background : Some chronic conditions may result from similar underlying mechanisms or may exacerbate lung disease suggesting the investigation of disease inter-relationships. We sought to determine if SOB was more common among adults with chronic conditions and to examine this association among those with and without asthma or COPD. Methods : In 2010 we conducted a cross-sectional mail survey of rural households as part of the Saskatchewan Rural Health Study. One adult per home provided information about each adult living in the home. There were 8261 adults from 4624 households (42% participation) included. We examined the associations between reported diagnosed chronic conditions (diabetes, cardiovascular disease, and sleep apnea) and SOB after adjusting for potential confounders and stratifying by history of doctor-diagnosed asthma or COPD. High SOB was defined by a score of ≥3 on the MRC breathlessness scale. Results : The respondents9 mean age was 56 years (SD=16 years) with 51% of the population being female. Approximately 14% had a MRC score ≥3. After adjustment, there was increased risk of high MRC score associated with the presence of diabetes [odds ratio (OR)=1.68, 95% confidence interval (CI)=1.32-2.14], cardiovascular disease (OR=2.18, 95%CI=1.80-2.65), and sleep apnea (OR=2.19, 95%CI=1.60-3.00). The associations with SOB were weaker among those with asthma or COPD with the exception of that for sleep apnea, which was stronger. Conclusions : Some conditions were associated with high SOB among those with and without a history of lung disease. These relationships may result from common pathways, possibly inflammatory, and may precede more serious chronic lung disease.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".