NICE's use of cost effectiveness as an exemplar of a deliberative process
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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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 | 0.161 | 0.304 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.006 | 0.065 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".