NICE uses wrong comparator to assess cost effectiveness of new drugs, report says
Bibliographic record
Abstract
Patients may be denied access to new drugs and medical innovation may be stalled by the way the National Institute for Health and Care Excellence assesses medicines for cost effectiveness, a report from the University College London School of Pharmacy has claimed.1 Licensed to Cure? said that in around a quarter of the cost effectiveness assessments of new medicines that NICE carried out from 2008 to the end of 2013 the manufacturers were asked to provide data on a non-licensed alternative against which their product could be compared. David Taylor, one of the authors of the report, said that the use of low cost non-licensed medicines as comparators was wrong and could create a false perception of the true value of a new medicine. “If you were to compare the cost of a new Volvo against an old banger, it’s very unlikely the Volvo would …
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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 | 0.149 | 0.545 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".