Hospitals in England see deficits grow as wage bills rise and earning capacity falls
Bibliographic record
Abstract
NHS hospitals in England are facing ballooning deficits as they struggle to maintain quality of care for growing numbers of patients, show the latest quarterly statistics from NHS Improvement.1 At the end of 2017, hospitals reported a deficit of £1.28bn (€1.45bn; $1.8bn), £365m above the planned level. The data cover the three months to 31 December, before the winter proper and a severe flu season that generated the highest number of flu related admissions to hospital since 2010-11. The flu peak was reached in January, so next quarter’s data, due to be published on 21 May, may bring worse news. However, preparations for winter seem to have had some positive effect. Comparing year to year, emergency departments’ performance remained well below target but got no worse: 89.5% of patients were seen within the target of four hours, against 89.6% in the corresponding quarter last year. But December was a poor month, with only 85.1% seen within the target, one …
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.067 | 0.021 |
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".