Adapting Tai Chi for Upper Limb Rehabilitation Post Stroke: A Feasibility Study
Bibliographic record
Abstract
Background: Tai chi (TC) has been reported as being beneficial for improving balance post stroke, yet its utility in upper limb rehabilitation remains unknown. Methods: Twelve chronic stroke survivors with persistent paresis of an upper limb underwent 60 minutes of adapted TC twice a week for eight weeks, with a 4-week follow up. A 10-min TC home program was recommended for the days without sessions. TC level of performance, attendance to the sessions, duration of self-practice at home, and adapted TC movements used were recorded. Results: Eleven participants completed the study. A clinical reasoning algorithm underlying the adaptation of TC was elaborated throughout the trial. Participants with varying profiles including a severely impaired upper limb, poor balance, shoulder pain, and severe spasticity were not only capable of practicing the adapted TC, but attended all 16 sessions and practiced TC at home for a total of 16.51 ± 9.21 h. The degree of self-practice for subgroups with low upper limb function, shoulder pain, or moderate-to-severe spasticity was similar to that of subgroups with greater upper limb function, no shoulder pain, and minimal-to-no spasticity. Conclusion: Adapted TC seems feasible for upper limb rehabilitation post stroke. Although the study was based on a small sample size and requires confirmation, low upper limb function, insufficient balance, spasticity, and shoulder pain do not appear to hinder the practice of TC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".