Modelling the long-term effects of an active case finding programme for undiagnosed COPD
Bibliographic record
Abstract
Introduction: More cases of undiagnosed COPD are found through active case-finding than routine care. The long-term cost-effectiveness of early diagnosis however remains unclear. Methods: Using data from the TargetCOPD case finding trial and published literature, a Markov model was built to evaluate the potential costs and benefits over a lifetime of implementing an active programme of case finding among ever smokers, screened initially at age 40 years at 3-yearly intervals, compared with routine care. Our model considered the natural progression of the disease in both undiagnosed and diagnosed patients, the effect of treatment and accounted for age, sex and incidence of COPD. The primary outcome of the analysis was the Incremental Cost Effectiveness Ratio (ICER) representing the additional cost incurred for every Quality Adjusted Life Year (QALY) gained. Probabilistic sensitivity analyses were undertaken. Results: Preliminary results suggest that an active case-finding programme was more cost-effective than routine practice (£10,996 per QALY) with 99% probability of being cost-effective at an ICER threshold of £20,000 per QALY gained. It became not cost-effective if response rates to postal questionnaires dropped from 32% to 9% or attendance at spirometry assessments reduced to 31% from 63%. The model was relatively insensitive to different estimates of treatment effects on disease progression, mortality and exacerbation rates. Conclusion: Three yearly active case finding is more cost-effective than routine practice over the lifetime of a high-risk population even under strongly conservative estimates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".