Timed Up and Go predicts functional decline in older patients presenting to the emergency department following minor trauma
Bibliographic record
Abstract
Background: there is no standardised test for assessing mobility in the Emergency Department (ED). Objective: we wished to evaluate the relationship between the Timed Up and Go (TUG) and frailty, functional decline and falls in community dwelling elders that present to the ED following minor trauma. Methods: this was a secondary analysis of a prospective cohort study conducted at eight Canadian hospitals. Evaluations included: TUG; Study of Osteoporotic Fractures Frailty Index; Older American Resources and Service Functional Scale; and self-reported falls. Of note, 3- and 6-month follow-up was conducted. Generalised linear model with log-binomial distribution was utilised. Relative risks (RR) and 95% CI were calculated. Results: TUG scores were available for 911/2918 patients, mean age 76.2 (SD 7.8) and 57.9% female. There was an association between TUG scores and frailty (P < 0.05) and functional decline at 3 (P < 0.05) and 6 (P < 0.05) months but not self-reported falls. For TUG scores 10-19 seconds, 20-29 seconds and ≥30 seconds, respectively: (i) frailty RR (95% CI): 1.8 (1.3-2.4), 3.0 (2.2-4.2) and 3.7 (2.6-5.1); (ii) functional decline RR (95% CI): 2.7 (1.1-6.4), 5.5 (2.1-14.3) and 8.9 (3.0-25.8); (iii) falls RR (95% CI): 0.9 (0.5-1.5), 1.3 (0.6-2.5) and 1.1 (0.4-3.5). Conclusion: in community dwelling elders presenting to the ED following minor trauma, TUG scores were associated with frailty and strongly associated with functional decline at 3 and 6 months post injury. TUG scores were not associated with self-reported falls. Use of the TUG in the ED will help identify frail patients at risk of functional decline.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".