Case-finding options for COPD: results from the Burden of Obstructive Lung Disease Study
Bibliographic record
Abstract
This study aimed to compare strategies for chronic obstructive pulmonary disease (COPD) case finding using data from the Burden of Obstructive Lung Disease study. Population-based samples of adults aged ≥40 yrs (n = 9,390) from 14 countries completed a questionnaire and spirometry. We compared the screening efficiency of differently staged algorithms that used questionnaire data and/or peak expiratory flow (PEF) data to identify persons at risk for COPD and, hence, needing confirmatory spirometry. Separate algorithms were fitted for moderate/severe COPD and for severe COPD. We estimated the cost of each algorithm in 1,000 people. For moderate/severe COPD, use of questionnaire data alone permitted high sensitivity (97%) but required confirmatory spirometry in 80% of participants. Use of PEF necessitated confirmatory spirometry in only 19-22% of subjects, with 83-84% sensitivity. For severe COPD, use of PEF achieved 91-93% sensitivity, requiring confirmatory spirometry in <9% of participants. Cost analysis suggested that a staged screening algorithm using only PEF initially, followed by confirmatory spirometry as needed, was the most cost-effective case-finding strategy. Our results support the use of PEF as a simple, cost-effective initial screening tool for conducting COPD case-finding in adults aged ≥40 yrs. These findings should be validated in real-world settings such as the primary care environment.
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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 | 0.017 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".