Bibliographic record
Abstract
In economic evaluation of healthcare interventions, the dominant practice is to calculate an incremental cost effectiveness ratio, usually based on the comparison of a new intervention against current practice. Canadian and UK health economists question the economic foundations of such an approach Who could resist implementing the results of a study showing that using alteplase (tPA; tissue plasminogen activator) rather than streptokinase in treatment of acute myocardial infarction costs $32 678 (£21 340; €33 330) per life year gained, which the authors declare to be “cost effective by customary criteria”?1 Despite similar claims from several such studies, the impact of economic evaluation on setting of priorities remains unclear.2–4 Among the reasons given for this are that opportunities for reducing costs while maintaining quality still arise, and that cost effectiveness analyses do not take all factors into account.3 Achieving the same result more cheaply—a success for economics—represents a classic cost effectiveness approach. The possibility that not all factors have been considered suggests that other approaches may make economic evaluation more relevant. We contend that, beyond the classic approach, many studies labelled as cost effectiveness analyses of health care are not really that at all. At best, this mislabelling is confusing: at worst, conclusions drawn by the studies' authors could be harmful to patients' health. Thus, there are contraindications to the use of cost effectiveness analysis in health care, and an alternative economic approach is required. In this paper we revisit the basic economic principles. Then we make the case that lack of adherence to such principles, through current practice of reducing everything to incremental cost effectiveness ratios, leads to contraindications. #### Summary points
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".