52 * THE PREVALENCE OF FRAILTY IN THE ACUTE GENERAL SURGICAL SETTING
Bibliographic record
Abstract
Introduction: Increasingly older and frailer patients are being referred to acute general surgical services. More and more of these people are subsequently undergoing surgical procedures. This is due to better surgical and anaesthetic skills, set in the context of increased patient expectation. However, the epidemiological evidence base for the older surgical patient is very poor, especially in acute general setting. In the UK, there has never been an assessment of the prevalence of frailty in this population. Methods: In three sites in Wales, England and Scotland comprising rural and urban populations, we studied consecutive patients aged over 65 years admitted to the acute surgery admissions ward. This was part of a wider surgical collaboration regarding surgical disease in the older person, www.opsoc.eu. We considered any older person admitted to the acute general surgical unit. We did not include patients with orthopaedic, urological, neurosurgical or vascular conditions. We assessed them for baseline demographic data. They were assessed for frailty using the 7 point clinical frailty score derived from the Canadian Study of Health and Ageing. Results: We collected data on 308 people, mean age 77.5 years (range 65–101), 177 (57.5%) were women. There were 29 (9.4%) classed as very fit, 66 (21.4%) well, 62 (20.1%) well with treated comorbid disease, 58 (18.8%) apparently vulnerable, 25 (8.1%) mildly frail, 44 (14.3%) moderately frail and 15 (4.9%) severely frail. Eight people had frailty data missing. Conclusions: In a large UK wide, representative sample of older people with acute general surgical disease nearly half of them were classed as apparently vulnerable or more severely frail.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".