NHS trusts cut overall deficit but remain at “breaking point,” leaders warn
Bibliographic record
Abstract
NHS trusts in England improved their financial position at the end of the first quarter of this year but remain under huge pressure from unprecedented demand, new figures have shown. The figures, published on 25 August by the regulator NHS Improvement,1 showed that the NHS provider sector recorded a combined deficit of £461m (€540m; $610m) in the first three months of 2016-17, £5m ahead of plan. This compared with a £930m deficit in the same quarter of last year. But the figures also showed a further sharp increase in demand, particularly at hospital emergency departments, which saw a 6.3% year on year rise in attendances and a 6.4% year on year rise in admissions in the first three months of 2016-17. The improvement in trusts’ financial position came after …
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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.019 | 0.017 |
| Insufficient payload (model declined to judge) | 0.038 | 0.017 |
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".