Timed up and go test and risk of falls in older adults: A systematic review
Bibliographic record
Abstract
OBJECTIVE: To assess the association and the predictive ability of the Timed Up and Go test (TUG) on the occurrence of falls among people aged 65 and older. METHODS: A systematic English Medline literature search was conducted on November 30, 2009 with no limit of date using the following Medical Subject Heading (MeSH) terms "Aged OR aged, 80 and over" AND "Accidental falls" combined with the terms "Timed Up and Go" OR "Get Up and Go". The search also included the Cochrane library and the reference lists of the retrieved articles. RESULTS: Of the 92 selected studies, 11 met the selection criteria and were included in the final analysis. Fall rate ranged from 7.5 to 60.0% in the selected studies. The cut-off time separating non-fallers and fallers varied from 10 to 32.6 seconds. All retrospective studies showed a significant positive association between the time taken to perform the TUG and a history of falls with the highest odds ratio (OR) calculated at 42.3 [5.1 - 346.9]. In contrast, only one prospective study found a significant association with the occurrence of future falls. This association with incident falls was lower than in retrospective studies. CONCLUSIONS: Although retrospective studies found that the TUG time performance is associated with a past history of falls, its predictive ability for future falls remains limited. In addition, standardization of testing conditions combined with a control of the significant potential confounders (age, female gender and comorbidities) would provide better information about the TUG predictive value for future falls in older adults.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".