The Timed Up and Go test: Predicting falls in ALS
Bibliographic record
Abstract
There are few functionally meaningful clinical measures used to guide management of patients with ALS. Falls are common, can be debilitating, and result in increased health care costs. We assessed the performance and ability to predict falls of the Timed Up and Go (TUG) test, which quantifies walking ability, in a prospective longitudinal study. Thirty-one patients underwent six monthly TUG, ALSFRS-R, forced vital capacity, muscle testing (MMT) and quality of life assessments. Linear and generalized linear mixed effects models assessed the associations among variables and ability to predict falls. The increase in TUG time was linear over six months, and TUG time was negatively associated with ALSFRS-R (p< or =0.001) and MMT scores (p< or =0.001). The TUG test was the only variable that was associated with the chance of falling (p = 0.024); patients with TUG times of 14 s had a 10% chance of falling during the study. In conclusion, TUG performance declined linearly in this longitudinal study, was correlated with standard outcome measures, and predicted falls. The TUG test can guide management of patients with ALS; a time of 14 s can be used to prompt the recommendation for mobility aids to prevent falls.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".