Making sense of cost-effectiveness analyses in respiratory medicine: a practical guide for non-health economists
Bibliographic record
Abstract
We live in a world of great advances in respiratory care, but at the same time, we are facing increasing budget constraints. In such a world, the use of any intervention is associated with “opportunity loss”: the benefit forgone by not using alternative interventions. Take the example of biologicals for severe asthma ( e.g. mepolizumab) or lung cancer ( e.g. nivolumab), with annual costs of around EUR 15 000 and >EUR 100 000 per patient, respectively. The concept of opportunity loss applies whenever decisions are made, either by physicians in clinical practice who have to decide which treatment patients receive, or by health policymakers during the approval process of new interventions for market entrance. If resources are spent on these medications, it means that there will be less budget available for other interventions. In respiratory medicine, interventions could involve pharmacological treatments, but also new bronchoscopic procedures, biomarker tests, diagnostics or other health technologies [1–4]. Cost-effectiveness analyses explained: their current and future role in respiratory policy decision making and daily clinical practice
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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 | 0.103 | 0.250 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.009 | 0.006 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".