Impact of cancers and cardiovascular diseases in chronic obstructive pulmonary disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cardiovascular disease and cancer are the two leading causes of morbidity and mortality in patients with chronic obstructive pulmonary disease. The epidemiological and mechanistic evidence linking these disorders, however, is uncertain. RECENT FINDINGS: In patients with mild chronic obstructive pulmonary disease, cardiovascular disease accounts for nearly 50% of all hospitalizations and over 20% of all deaths, whereas lung cancer accounts for about a third of all mortality. Collectively, cancer is responsible for over 50% of all deaths in mild chronic obstructive pulmonary disease. In general, chronic obstructive pulmonary disease increases the risk of cardiovascular disease and lung cancer by two-fold, with increasing risk as the disease progresses. The mechanisms linking these disorders have not been well worked out. Shared genetic risk factors, and perturbations in the inflammatory, oxidative and neurohumoral responses, are implicated. Epidemiological studies suggest that anti-inflammatory drugs may reduce the risk of cardiovascular disease and lung cancer but have not been confirmed. SUMMARY: Cardiovascular diseases and lung cancer are major sources of morbidity and mortality in chronic obstructive pulmonary disease. Patients should be assessed carefully for additional risk factors and be treated aggressively with interventions to mitigate the risk of cardiovascular disease and lung cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".