Bibliographic record
Abstract
As the war of words over NHS financing becomes increasingly bitter,12 the statistics that track its performance have acquired a prominence they last enjoyed in the late 1990s, when Tony Blair’s first government was struggling to meet its promise to cut waiting lists. A huge quantity of data are published by the NHS in England covering all parts of the service. As in the 1990s, the keenest attention is always paid to hospital performance, where data on key measures are published monthly. The latest release, covering the period to the end of November 2016, appeared on 12 January.3 The NHS Constitution sets the standard that 95% of patients attending hospital accident and emergency departments should be seen, admitted, or discharged within four hours. In July 2016 NHS Improvement changed the rules slightly, saying that for 2016-17 the aim of hospital trusts should be to improve so that by quarter 4 they could once more meet the standard. So they will be judged in the short term by their rate of improvement, rather than by whether they hit the target. In …
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.115 | 0.025 |
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".