Perspectives on the National Institute for Health and Clinical Excellence's recommendations to use health technologies only in research
Bibliographic record
Abstract
BACKGROUND: The concept of using public funds to pay for healthcare interventions only when provided in the context of ongoing research is receiving increasing attention worldwide. Nevertheless, these decisions are often controversial and implementation can be problematic. OBJECTIVES: The aim of this study was to investigate the views of United Kingdom stakeholders on the current arrangements for implementing "only in research" (OIR) decisions and to investigate how improvements might be made. METHODS: After an internal review of previous OIR decisions issued by the National Institute for Health and Clinical Excellence (NICE), deliberations by NICE's Citizens Council, and an international workshop convened by NICE and the United States Agency for Healthcare Research and Quality, thirteen key stakeholders and experts from academia, industry, government, and the National Health Service (NHS) were interviewed using a semistructured interview guide. Interview transcripts were subjected to a framework-based analysis using computer-assisted qualitative data analysis software. RESULTS: All interviewees endorsed the use of the OIR option. There was a high degree of consensus for several suggestions regarding how the use of the OIR option might be improved. For example, there was universal agreement that a formal process should be established to prioritize research needs arising from OIR decisions and that funds for publicly funded research projects should be channeled in a manner that would better motivate healthcare providers to participate in OIR-related research. CONCLUSIONS: The findings of this study suggest several potential modifications of the OIR pathway in the United Kingdom and may also be helpful to health technology assessment agencies in other countries that already use or are considering using an OIR-like option to reduce the uncertainty inherent in health technology assessment.
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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 | 0.422 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.017 | 0.049 |
| Scholarly communication | 0.055 | 0.029 |
| Open science | 0.013 | 0.039 |
| Research integrity | 0.086 | 0.060 |
| Insufficient payload (model declined to judge) | 0.004 | 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".