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 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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.020 | 0.028 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".