A multicentre survey investigating the knowledge, behaviour, and attitudes of surgical healthcare professionals to frailty assessment in emergency surgery: DEFINE(surgery)
Bibliographic record
Abstract
PURPOSE: Screening for frailty in people admitted with emergency surgical pathology can initiate timely referrals to enhanced perioperative services such as intensive care and geriatric medicine. However, there has been little research exploring surgical healthcare professionals' opinions to frailty assessment, or accuracy in identification. This study aimed to assess the knowledge, behaviour, and attitudes of healthcare professionals to frailty assessment in emergency surgical admissions. METHODS: We designed a cross-sectional multicentre study developed by a multiprofessional team of surgeons, geriatricians, and supported by patients. A semi-structured survey examined attitudes and behaviours. Knowledge was assessed by comparing respondents' accuracy in scoring twenty-two surgical case vignettes using the Clinical Frailty Scale. RESULTS: Eleven hospitals across England, Wales, and Scotland participated. Two hundred and eleven clinicians responded-20.4% junior doctors, 43.6% middle grade doctors, 24.2% senior doctors, 11.4% nurses and physician associates. Respondents strongly supported perioperative frailty assessment. Most were already assessing for frailty, although frequently not using a standardised tool. There was a strong call for more frailty education. Participants scored 2175 vignettes with 55.4% accurately meeting the gold standard; accuracy improved to 87.3% when categorised into "not frail/mildly frail/severely frail" and 94% when dichotomised to "not frail/frail". CONCLUSION: Frailty assessment is well supported by healthcare professionals working in surgery. However, standardised tools are not routinely being used, and only half of respondents could accurately identify frailty. Better education around frailty assessment is needed for healthcare professionals working in surgery to improve perioperative pathway for people living with frailty.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".