NICE's use of cost effectiveness as an exemplar of a deliberative process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper seeks to test 12 conjectures about the predicted use of deliberative processes by applying them to the technology assessment procedures used by the National Institute for Health and Clinical Excellence (NICE) in England and Wales. A deliberative process is one that elicits and combines evidence of different kinds and from different sources in order to develop guidance - in the present case, guidance for a health care system. A deliberative process entails the integration of three kinds of evidence: scientific context-free evidence about the general clinical potential of a technology, scientific context-sensitive evidence about particular evidence in realistic scenarios, and colloquial evidence to fit context-free scientific evidence into a context and to supply the best evidence short of scientific evidence to fill in any relevant gaps. It is shown that NICE's appraisals procedures and, in particular, its approach to cost effectiveness, entail both the weighing of each of these types of evidence and can be seen as rational responses to the 12 conjectures.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it