Bibliographic record
Abstract
Conventional approaches to estimating cost-effectiveness thresholds do not take into account the allocation of any welfare gain from new technologies between patients (‘consumer surplus’) and manufacturers (‘producer surplus’). Existing approaches also do not consider alternative policy objectives regarding this allocation and the implications of strategic behaviour by manufacturers (‘pricing to the threshold’). This paper proposes a new conceptual model that incorporate these considerations. A new conceptual model of the threshold was developed that incorporates strategic behaviour by manufacturers. The model accounts for the cost of developing technologies and the implications of patents and other barriers to entry (which allow for super-normal profits). The ‘optimal threshold’ is derived for each of several policy objectives regarding the distribution of welfare between patients and manufacturers. Where the policy objective is to maximize consumer surplus, the optimal threshold is lower than that implied by a conventional ‘supply-side’ approach. Where the objective is to maximize producer surplus, the optimal threshold is infinitely high, but consumer surplus is negative. Where the objective is to ensure both consumer and producer surplus are positive, the optimal threshold lies above the consumer surplus-maximizing threshold but below a conventional ‘supply-side’ threshold. If policy makers desire that patients share some of the welfare gain from new technologies, the threshold should be lower than implied by existing theoretical approaches. Thresholds currently used in practice are also too high, resulting in negative consumer surplus. This work has implications for policy making and future empirical research into the threshold.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".