Predictors of daily mobility skills 6 months post-discharge from acute care or rehabilitation in older adults with stroke living at home
Bibliographic record
Abstract
PURPOSE: To determine the evolution of daily mobility skills from the timed up-and-go (TUG) upto 6 months after home return in older adults with stroke discharged from acute care or rehabilitation; and to identify the best predictive factors of the TUG at 6 months post-discharge. METHODS: In this longitudinal prospective study, people with stroke aged 65 years or more and discharged home from an acute care hospital (n = 82) or a rehabilitation service (n = 109) were included. The TUG was measured at discharge (T1), and at 3 and 6 months post-discharge (T2 and T3). Correlations between the TUG at T3 and sociodemographic and clinical variables, as well as physical, cognitive, perceptual and psychological measures at T1, were used in a multiple regression model to identify the best predictors of TUG at T3. RESULTS: TUG did not change between T1, T2 and T3 in the two groups of participants. The best predictors of TUG at T3 in participants from acute care were the use of a walking aid in daily life, age, deficits in oral expression and the presence of depressive symptoms. In participants from rehabilitation, predictors were the stage of motor recovery of the foot, the use of a walking aid in daily life, number of schooling years and memory impairments. CONCLUSION: Daily mobility skills, as assessed with the TUG, did not deteriorate upto 6 months after home return in older adults with stroke. The best predictor of the TUG at T3 is the use of a walking aid during daily life in participants from acute care, and motor recovery of the foot in participants from rehabilitation.
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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 | 0.004 |
| 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 | 0.001 | 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".