Mortality in COPD: role of comorbidities
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) represents an increasing burden throughout the world. COPD-related mortality is probably underestimated because of the difficulties associated with identifying the precise cause of death. Respiratory failure is considered the major cause of death in advanced COPD. Comorbidities such as cardiovascular disease and lung cancer are also major causes and, in mild-to-moderate COPD, are the leading causes of mortality. The links between COPD and these conditions are not fully understood. However, a link through the inflammation pathway has been suggested, as persistent low-grade pulmonary and systemic inflammation, both known risk factors for cardiovascular disease and cancer, are present in COPD independent of cigarette smoking. Lung-specific measurements, such as forced expiratory volume in one second (FEV(1)), predict mortality in COPD and in the general population. However, composite tools, such as health-status measurements (e.g. St George's Respiratory Questionnaire) and the BODE index, which incorporates Body mass index, lung function (airflow Obstruction), Dyspnoea and Exercise capacity, predict mortality better than FEV(1) alone. These multidimensional tools may be more valuable because, unlike predictive approaches based on single parameters, they can reflect the range of comorbidities and the complexity of underlying mechanisms associated with COPD. The current paper reviews the role of comorbidities in chronic obstructive pulmonary disease mortality, the putative underlying pathogenic link between chronic obstructive pulmonary disease and comorbid conditions (i.e. inflammation), and the tools used to predict chronic obstructive pulmonary disease mortality.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".