External validation of exacerbation and mortality outcomes of health economic decision models for COPD
Bibliographic record
Abstract
Background: Health economic decision models have become increasingly important to estimate the long-term effects and costs of COPD treatments. We aimed to validate the clinical outcomes of currently available COPD models against results of the 3-yr TORCH and 4-yr UPLIFT trial. Methods: COPD modelling groups participating in a health economic modelling network simulated exacerbations and mortality for the placebo groups of the two trials. Second, relative reductions in annual decline in lung function and exacerbation frequency as observed in the TORCH salmeterol/fluticasone group and the UPLIFT tiotropium group compared to placebo were applied to simulate treatment effectiveness. Finally, the cost per exacerbation avoided and the cost QALY gained for both treatments were estimated. Results: Three of the six participating models found higher total annual exacerbation rates (1.22 to 1.48) compared to the TORCH trial (1.13). Four models reported higher rates (1.13 to 1.52) than the UPLIFT trial (0.85). Two models reported higher mortality rates than the TORCH (15.2%) (models: 20.0% and 30.6%) and UPLIFT trial (16.3%) (24.8% and 36.0%), while one model reported lower rates compared to both trials (9.8% and 12.1%). Simulation of treatment effectiveness showed that the absolute reduction in total exacerbations, the gain in QALYs and the cost-effectiveness ratios did not differ from the trials, except for one model. Conclusion: Although the majority of current health economic decision models for COPD reported higher exacerbation rates than observed in two clinical trials, estimates of the absolute treatment effect and cost-effectiveness ratios were similar to the trials in most models.
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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 | 0.069 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".