Bibliographic record
Abstract
The National Institute for Clinical Excellence (NICE) was established in England and Wales in 1999 to “provide guidance to the NHS on the use of selected new and established technologies.”1 NICE synthesises evidence on the effectiveness and cost of treatments and reaches “a judgment as to whether, on balance, the intervention can be recommended as a cost-effective use of NHS resources.”1 How has the institute measured up to these ambitious goals, and what has been learnt about the demands of an explicit process for assessing health technology? The institute attracted attention from the international media with its first judgment that “health professionals should not prescribe zanamivir (Relenza) during the 1999/2000 influenza season.”2 The additional cost to the NHS would have been about £10m ($15m) for the benefit of reducing episodes of flu from six days to five. Although subsequently revised,3 the decision showed that the institute has teeth and is prepared to bite even home grown drug companies like GlaxoWellcome (now GlaxoSmithKline). In some places, such as Australia4 and Ontario, Canada,5 pharmaceutical companies must prove that their products are cost effective before they can be reimbursed by the government. Although NICE operates differently in that it does not automatically assess new products and …
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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 | 0.062 | 0.300 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.022 | 0.034 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".