109 * CAN A FRAILTY SCALE BE USED TO TRIAGE ELDERLY PATIENTS FROM EMERGENCY DEPARTMENT TO GERIATRIC WARDS?
Bibliographic record
Abstract
Background: There is no widely established method for triaging elderly, frail patients to geriatric wards. In our teaching hospital, informal methods are currently used to allocate patients. We aimed to assess the potential impact of introducing the Clinical Frailty Scale (CFS) (Rockwood, K., Song, X., MacKnight, C., Canadian Medical Association Journal (2005) 173, pp.489-495) as a triaging method for patients aged over 75 who are admitted via the Emergency Department (ED). The CFS is a rapid, simple case-finding tool which might be used to improve the proportion of frail patients who are identified and allocated to geriatric wards. Methods: We applied the Clinical Frailty Scale to 118 elderly patients who had been admitted from ED over a two-week period. We compared the distribution of frailty in geriatric and non-geriatric wards, and measured the strength of the CFS to identify frail people, compared to other frailty scales i.e. reported Edmonton Frailty Scale (rEFS) (Hilmer, S.N., Perera, V., Mitchell, S., Australasian Journal on Ageing (2009) 28(4) pp.182-188, PRISMA-7, Identification of Seniors at Risk (ISAR) (Dendukuri, N., McCusker, J., Belzile, E. Journal of the American Geriatrics Society (2004) 52(2) pp.290-296). Results: The current difference between frailty in geriatric and non-geriatric medical wards in patients aged over 75 was not statistically significant (Standard deviation = 1.84 (geriatric), 2.10 (non-geriatric, p = 0.58).Analysis of receiver operating curves showed that the Clinical Frailty Scale accurately identified frail patients when compared to other well-validated frailty scales at appropriate cut-off points (rEFS = 9 + , Area under curve (AUC) = 89.1%, standard deviation (SD) = 3%) (ISAR = 3 + , AUC = 81.7%, SD = 3.9%) (PRISMA-7 = 2 + , AUC = 90.8%, SD = 3.1%). Conclusions: Implementation of the CFS as a triage tool for elderly patients at ED could increase the proportion of frail patients who are directly admitted to a geriatric ward. This could improve patient access to appropriate geriatric care.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".