Evidence and Quality, Practicalities and Judgments: Some Experience from NICE
Bibliographic record
Abstract
The National Institute for Health and Clinical Excellence (NICE) is the principal provider of information about the evidence relating to effectiveness and cost-effectiveness in healthcare in the National Health Service of England and Wales. NICE regards quality as primarily to do with effectiveness, safety and the patient experience. In this paper we comment on the quality of evidence regarding these three and speculate about the consequences of widening the range of interventions for appraisal and taking more complete account of upstream determinants of health. We also comment on the type and quality of the evidence, as well as the way in which it is used, and the values--too often hidden--that permeate both the evidence and the way in which it is used.
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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 | 0.537 | 0.673 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.005 | 0.036 |
| Scholarly communication | 0.029 | 0.021 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.019 | 0.030 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".