Relationship between timed ‘up and go’ and gait time in an elderly orthopaedic rehabilitation population
Bibliographic record
Abstract
OBJECTIVE: To analyse the relationship between the timed 'up and go' (TUG) and gait time in an elderly orthopaedic population, in order to determine whether additional useful information is obtained by measuring both. DESIGN: Observational study. SETTING: Jewish Rehabilitation Hospital, Laval, Quebec. SUBJECTS: Seventy-nine consecutive inpatients in the orthopaedic programme with a primary admitting diagnosis of either total hip replacement (THR), total knee replacement (TKR), or hip fracture repair. OUTCOME MEASURES: Timed 'up and go' and time to walk 10 metres. RESULTS: The admission correlation between gait time and TUG was r= 0.745. The correlation at discharge (r= 0.816) was higher than that seen on admission. The relationship between gait time and TUG was linear both at admission and discharge. The correlation between gait time and TUG was strong for patients with TKR at admission (r= 0.868) and discharge (r= 0.878), and for patients with THR, both at admission (r= 0.809) and discharge (r= 0.879). However, the correlation on admission was weaker for patients with hip fracture (r= 0.497). For slow walkers (people with a gait speed below 0.5 m/s) on admission, the correlation was moderate (r= 0.649). However, for those with gait speeds faster than or equal to 0.5 m/s, the correlation was weaker (r= 0.484). This discrepancy was no longer evident on discharge. Likewise, for patients with a fast TUG score (< 30 seconds) on admission, there was virtually no relationship between TUG and gait time (r= 0.084), although a good correlation was present for those with 'up and go' times longer than 30 seconds (r = 0.634). As with gait speed, this difference disappeared by discharge. CONCLUSIONS: The relationship between gait time and TUG in an elderly orthopaedic rehabilitation population is good, and its strength varies by specific diagnosis, mobility, and time point in the course of therapy. The two measures are not redundant in this population.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".