Bibliographic record
Abstract
Abstract No country can afford all the health care interventions that might benefit patients. Demand will always outstrip available resources, so priorities have to be agreed upon. Such decisions are controversial, making it vital that they are underpinned by robust transparent processes and methods. In the United Kingdom, this is the responsibility of the National Institute for Health and Clinical Excellence (NICE). In 2009, in response to challenges that NICE was not giving sufficient value to innovation, an independent enquiry was undertaken by Sir Ian Kennedy. The enquiry raised important questions about whether NICE should only offer incentives for innovation when the benefits are actually seen by the National Health Service (NHS) as improved outcomes for patients, or whether future, but as yet unrealized, benefits such as the subsequent development of the next generation of drugs should be taken into account. There is a UK government commitment to value‐based pricing but questions remain about how this could value innovation. Potential solutions are an increased use of NICE's “only in research” recommendations and exploration of novel trial designs. Drug Dev Res 71: 449–456, 2010. © 2010 Wiley‐Liss, Inc.
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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 | 0.042 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".