Bibliographic record
Abstract
A dramatic increase in deaths in the UK since 2011 has defied actuarial forecasts. This has led some to propose a direct link between the rise in deaths and government social care austerity. However, several facts argue against this link. Firstly, age standardised mortality in the second quarter of 2019 was statistically lower than the second quarter in all years since 2001, clearly austerity is still present, but age standardised mortality has recovered. Also, deaths have increased equally across the whole of the UK, whereas social care austerity has largely been restricted to England. English citizens who are residents outside of the UK also show the same trend. These effects are highly reminiscent of a recurring series of disease outbreaks of an unidentified pathogen. In addition, increases in deaths are always linked to increases in hospital admissions. This link arises since around half of a person's lifetime use of acute services is compressed into the last 6 months of life, irrespective of the age at death. This is called the nearness-to-death effect. International research is needed to understand exactly why deaths are behaving in this unique way. While austerity has created a significant problem relating to delayed discharges in hospitals and has highlighted serious problems with how end-of-life care is to be funded, it seemingly cannot be blamed for the increased mortality rate.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".