COPD and cardiovascular diseases: now is the time for action!
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of mortality worldwide, responsible for 31% of all deaths and accounting for 18 million deaths annually.1 Chronic obstructive pulmonary disease (COPD), which is characterised by airflow limitation that is usually progressive and associated with persistent small airway inflammation, is an important (though frequently under-recognised) risk factor for CVDs.2 For example, the population attributable risk of COPD for mortality related to ischaemic heart disease is approximately 30%, independent of the effects of cigarette smoking.3 Indeed, some studies have shown that the risk imposed by COPD for CVDs may be as large as (if not larger than) those related to well-established CVD risk factors such as hypertension and hypercholesterolaemia.3 A more recent study showed that COPD is associated with increased risk of sudden cardiac deaths, with the risk increasing by more than threefold among patients with COPD who have a history of frequent exacerbations.4 In this issue of Thorax , Morgan and colleagues extend our current understanding of the relationship between COPD and CVDs by demonstrating that COPD is a significant risk factor for 12 different CVD conditions including angina, myocardial infarction (MI), heart failure, sudden cardiac arrest, atrial fibrillation, abdominal aortic aneurysm, peripheral arterial disease, pulmonary arterial hypertension, ischaemic stroke, haemorrhagic stroke and transient ischaemic attacks.5 Most importantly, they showed that the highest risks were observed among relatively young individuals with COPD (aged 35–54 years) with HRs related to …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".