On the margins of health economics: a response to ‘resolving NICE’S nasty dilemma’
Bibliographic record
Abstract
In a 2011 article published in this journal, Baker et al. set out to resolve a nasty dilemma for NICE by reconciling two approaches for determining whether adopting a new intervention would increase total health gains produced from available resources and hence increase system efficiency. In this response we show how the proposed reconciliation, as well as the two approaches on which it is based, fail to inform decision makers about the efficiency of a new intervention. We show how this arises from the misuse of incremental costs and effects of between-intervention comparisons as measures of changes in costs and effects associated with marginal adjustments to the scale of an intervention. Ironically, incremental data represent the choices faced by decision makers and we illustrate a method for determining unambiguously whether a new intervention represents an improvement in efficiency.
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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 | 0.065 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.036 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.168 | 0.145 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".