Trends in chronic obstructive pulmonary disease in the Asia-Pacific regions
Bibliographic record
Abstract
PURPOSE OF REVIEW: The burden of chronic obstructive pulmonary disease (COPD) is rapidly growing in the Asia-Pacific region. There is the need for region-specific research and analysis of the epidemiology of COPD to raise awareness of the disease and highlight its causes. Such information is essential to for the development of effective national health policies to ensure evidence-based deployment of finite healthcare resources in the prevention and management of COPD. RECENT FINDINGS: Recent population-based epidemiological studies have confirmed previous assumptions that COPD in the Asia-Pacific region is as prevalent as in the mature economies of the western world. The greatest numbers of deaths and hospitalizations from COPD are concentrated in this populous region of the world. The patterns in trends in mortality and hospitalization in the past 10 years in Asia-Pacific countries show a spectrum from the 'mature' to the 'evolving' and are likely related to the combined effects of cigarette smoking and nonsmoking risk factors. Gross underdiagnosis of COPD and underutilization of spirometry further contribute to burden and are barriers to appropriate and timely management of COPD. SUMMARY: COPD is a common disease with a large disease burden throughout the Asia-Pacific region. Effective public health preventive measures coupled with timely case detection are needed for the reversal of trends and the reduction of disease burden.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".