Cardiovascular disease and COPD: dangerous liaisons?
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD) frequently occur together and their coexistence is associated with worse outcomes than either condition alone. Pathophysiological links between COPD and CVD include lung hyperinflation, systemic inflammation and COPD exacerbations. COPD treatments may produce beneficial cardiovascular (CV) effects, such as long-acting bronchodilators, which are associated with improvements in arterial stiffness, pulmonary vasoconstriction, and cardiac function. However, data are limited regarding whether these translate into benefits in CV outcomes. Some studies have suggested that treatment with long-acting β 2 -agonists and long-acting muscarinic antagonists leads to an increase in the risk of CV events, particularly at treatment initiation, although the safety profile of these agents with prolonged use appears reassuring. Some CV medications may have a beneficial impact on COPD outcomes, but there have been concerns about β-blocker use leading to bronchospasm in COPD, which may result in patients not receiving guideline-recommended treatment. However, there are few data suggesting harm with these agents and patients should not be denied β-blockers if required. Clearer recommendations are necessary regarding the identification and management of comorbid CVD in patients with COPD in order to facilitate early intervention and appropriate treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".