What can we learn from 68 000 clinical frailty scale scores? Evaluating the utility of frailty assessment in emergency departments
Bibliographic record
Abstract
BACKGROUND: Emergency departments (EDs) in England are under significant strain, with increasing attendances and extended wait times, affecting frail older adults. The clinical frailty scale (CFS) has been implemented as a tool to assess frailty in ED settings, but its reliability and predictive accuracy as a screening tool remain debated. OBJECTIVE: To evaluate the use and variability of the CFS in EDs and its association with patient outcomes, including discharge rates, length of stay, readmission and mortality. METHODS: A retrospective cohort study of ED attendances at two London (UK) hospitals from 2017 to 2021. Data included CFS scores, demographics, clinical observations and outcomes. Comparative statistics, logistic regression, Cox proportional hazards models and competing risk regression were applied to examine CFS predictive validity. RESULTS: In a sample of 123 324 ED visits, CFS scores strongly correlated with adverse outcomes: e.g. for long-term mortality (n = 33 475, events = 8871), each CFS single-point increase was associated with a 25% increase in mortality risk (95% CI 1.23-1.26). CFS scores varied significantly between raters and across visits, median difference two levels (interquartile range 1-3). Intraclass correlation coefficient analysis showed that 33.1% of CFS score differences was attributable to between-patient differences, 15.4% to inter-rater differences, with 51.5% residual variance from non-frailty factors, such as acute illness severity. CONCLUSION: The CFS is associated with crucial patient outcomes in the ED. Inter-rater variability and potentially confounding factors can limit its consistency. Automation to enhance CFS score reliability should be explored as a means to support proactive management.
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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 | 0.028 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".