Core requirements of frailty screening in the emergency department: an international Delphi consensus study
Bibliographic record
Abstract
INTRODUCTION: Frailty is associated with adverse outcomes among patients attending emergency departments (EDs). While multiple frailty screens are available, little is known about which variables are important to incorporate and how best to facilitate accurate, yet prompt ED screening. To understand the core requirements of frailty screening in ED, we conducted an international, modified, electronic two-round Delphi consensus study. METHODS: A two-round electronic Delphi involving 37 participants from 10 countries was undertaken. Statements were generated from a prior systematic review examining frailty screening instruments in ED (logistic, psychometric and clinimetric properties). Reflexive thematic analysis generated a list of 56 statements for Round 1 (August-September 2021). Four main themes identified were: (i) principles of frailty screening, (ii) practicalities and logistics, (iii) frailty domains and (iv) frailty risk factors. RESULTS: In Round 1, 13/56 statements (23%) were accepted. Following feedback, 22 new statements were created and 35 were re-circulated in Round 2 (October 2021). Of these, 19 (54%) were finally accepted. It was agreed that ideal frailty screens should be short (<5 min), multidimensional and well-calibrated across the spectrum of frailty, reflecting baseline status 2-4 weeks before presentation. Screening should ideally be routine, prompt (<4 h after arrival) and completed at first contact in ED. Functional ability, mobility, cognition, medication use and social factors were identified as the most important variables to include. CONCLUSIONS: Although a clear consensus was reached on important requirements of frailty screening in ED, and variables to include in an ideal screen, more research is required to operationalise screening in clinical practice.
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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 | 0.228 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".