Age of patients undergoing surgery
Bibliographic record
Abstract
BACKGROUND: Advancing age is independently associated with poor postoperative outcomes. The ageing of the general population is a major concern for healthcare providers. Trends in age were studied among patients undergoing surgery in the National Health Service in England. METHODS: Time trend ecological analysis was undertaken of Hospital Episode Statistics and Office for National Statistics data for England from 1999 to 2015. The proportion of patients undergoing surgery in different age groupings, their pooled mean age, and change in age profile over time were calculated. Growth in the surgical population was estimated, with associated costs, to the year 2030 by use of linear regression modelling. RESULTS: Some 68 205 695 surgical patient episodes (31 220 341 men, 45·8 per cent) were identified. The mean duration of hospital stay was 5·3 days. The surgical population was older than the general population of England; this gap increased over time (1999: 47·5 versus 38·3 years; 2015: 54·2 versus 39·7 years). The number of people aged 75 years or more undergoing surgery increased from 544 998 (14·9 per cent of that age group) in 1999 to 1 012 517 (22·9 per cent) in 2015. By 2030, it is estimated that one-fifth of the 75 years and older age category will undergo surgery each year (1·49 (95 per cent c.i. 1·43 to 1·55) million people), at a cost of €3·2 (3·1 to 3·5) billion. CONCLUSION: The population having surgery in England is ageing at a faster rate than the general population. Healthcare policies must adapt to ensure that provision of surgical treatments remains safe and sustainable.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".