Co-morbidity in Mild-to-Moderate COPD: Comparison to Normal and Restrictive Lung Function
Bibliographic record
Abstract
BACKGROUND: A relationship between local and systemic inflammation and different co-morbidities, such as cardiovascular, has been discussed in relation to disease process and prognosis in COPD. AIM: To evaluate if conditions as cardiovascular diseases, diabetes, chronic rhinitis and gastroesophageal reflux are overrepresented in COPD. METHODS: All subjects with COPD according to GOLD, FEV(1)/FVC<0.70, were identified (n = 993) from the clinical follow-up in 2002-04 of the OLIN (Obstructive Lung Disease in Northern Sweden) studies' cohorts together with 993 gender- and age-matched reference subjects without COPD (non-COPD, further divided into normal and restrictive lung function). Interview data on co-morbidity and symptoms were used. RESULTS: Cardiovascular co-morbidity, taken together heart disease, hypertension, stroke and intermittent claudication, was the most common and higher in COPD compared to in normal lung function (Nlf) 50.1% vs 41.0% (p<0.001). The prevalence of chronic rhinitis and gastroesophageal reflux (GERD) was higher in COPD compared to in Nlf (43.1% vs 32.3%, p<0.001 and 16.7% vs 12.0%, p = 0.011). In restrictive lung function the prevalence of chronic rhinitis, cardiovascular disease, hyperlipemia and diabetes was higher compared to in Nlf (41.0% vs 32.3%, p = 0.017, 59.0% vs 41.0%, p<0.001, 29.2% vs.12.9%, p = 0.033, 20.9% vs 8.6%, p <0.001). In COPD and heart disease, 62.5% had chronic rhinitis and/or GERD, while in Nlf the corresponding proportion was 42.5%. CONCLUSION: Co-morbid conditions such as cardiovascular disease, chronic rhinitis and gastroesophageal reflux were common in COPD. The overlap between heart disease, chronic rhinitis and GERD was large in COPD. Restrictive lung function did also identify a population with increased disease burden.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".