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Record W4394945349 · doi:10.1007/s41999-024-00962-7

A multicentre survey investigating the knowledge, behaviour, and attitudes of surgical healthcare professionals to frailty assessment in emergency surgery: DEFINE(surgery)

2024· article· en· W4394945349 on OpenAlexaff
Philip Braude, F Parry, Katherine Warren, Emma Mitchell, Kathryn McCarthy, Rachel G. Khadaroo, Benjamin Carter

Bibliographic record

VenueEuropean Geriatric Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsUniversity of Alberta
FundersNorth Bristol NHS TrustBritish Geriatrics SocietyNHS Greater Glasgow and Clyde
KeywordsMedicineHealth professionalsHealth careEmergency surgerySurgeryMedical emergency

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.080
GPT teacher head0.391
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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