Bibliographic record
Abstract
In early October 1999, Sir Richard Sykes, the chairman of Glaxo Wellcome, then Britain’s biggest pharmaceutical company, stormed into 10 Downing Street. He was incandescent. A body that most people had not heard of – the National Institute for Clinical Excellence (NICE) – in its very first decision had just recommended that the National Health Service in England should not prescribe what the company had expected to be its next big money-spinner. A treatment for influenza, known as Relenza. This was the first major challenge of the budding What Works movement – when NICE, which came to be known as the first What Works Centre – first refused to support a drug set to be popular and profitable. In this chapter, Nick Timmins recounts this story, as well as a broader picture of how NICE was created, established itself, and how it has evolved between then and now.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.031 | 0.033 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.017 | 0.034 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".