Truncal impairment after stroke: clinical correlates, outcome and impact on ambulatory and functional outcomes after rehabilitation
Bibliographic record
Abstract
INTRODUCTION: Good trunk performance is important for activities such as sitting and standing. In a cohort of patients with stroke, we sought to evaluate changes in trunk performance after stroke, establish factors correlated to trunk performance and assess the impact of trunk performance on discharge ambulatory and functional status. METHODS: This was a retrospective review of the data of patients with stroke admitted to Tan Tock Seng Hospital rehabilitation centre, Singapore, over a two-year period. Data analysed included the National Institutes of Health Stroke Scale (NIHSS), Montreal Cognitive Assessment (MOCA), Fugl-Meyer Assessment (FMA) of limb motor impairment and Functional Independence Measure-motor (FIM-motor) scores, which measures self-care ability. Trunk performance was assessed on the Trunk Impairment Scale (TIS). RESULTS: 577 patients with stroke (mean age 63.2 ± 11.8 years) were analysed. Truncal impairment was present in 96.4% of patients. Mean admission TIS score was 14.3 ± 6.1 and this improved to 17.2 ± 5.2 on discharge (p < 0.001). Admission TIS score was positively correlated with admission MOCA, FMA-upper limb and FMA-lower limb scores, and negatively correlated to NIHSS score and neglect. Admission TIS scores significantly predicted discharge FIM-motor scores (p < 0.001) and ambulatory status (p < 0.001). CONCLUSION: Truncal impairment was common and improvements in trunk performance were seen after rehabilitation. Trunk performance was significantly correlated to stroke severity, upper and lower limb motor power, cognition and neglect. As admission trunk performance predicted discharge functional and ambulatory status, it is recommended that trunk performance be evaluated for all patients with stroke.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".