Health Technology Assessment in the Cost-Disutility Plane
Bibliographic record
Abstract
Previously, comparisons of multiple strategies in health technology assessment have been undertaken on the incremental cost-effectiveness plane using efficiency frontiers and cost-effectiveness acceptability curves. This article proposes shifting the comparison of multiple strategies to the cost-disutility plane. Evidence-based decision making requires comparison of all strategies against each other. Consequently, the origin in the incremental cost-effectiveness plane cannot be the appropriate reference point in comparing multiple nondominated strategies. A linear transformation onto the cost-disutility plane allows an equivalent comparison of net benefit and permits the use of standard efficiency measurement methods to estimate 1) the degree of dominance (technical inefficiency) of dominated strategies and 2) the net benefit inefficiency (i.e., losses in net benefit relative to an optimal strategy). In comparing strategies under uncertainty, a comparison of loss in net benefit leads to the expected net loss frontier, which, unlike cost effectiveness acceptability curves, directly identifies differences in expected net benefit (net loss) and the expected value of perfect information. Thus, decision makers can be better informed about the choice of optimal strategy and the potential value of future research to resolve uncertainty. Comparing strategies in the cost-disutility plane is suggested to better inform decision making and to provide a link between the cost-effectiveness literature and efficiency measurement methods.
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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 | 0.066 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".