Physical Therapy for an Adult with Chronic Stroke after Botulinum Toxin Injection for Spasticity: A Case Report
Bibliographic record
Abstract
PURPOSE: In this case report, we describe the type and duration of a physical therapy and botulinum toxin type A (BoNTA) intervention directed at lower limb spasticity and the gait and balance improvement in a patient post-stroke. Treatment of focal spasticity with BoNTA intramuscular injections combined with physical therapy is recommended by rehabilitation experts. However, the optimal type and duration of physical therapy intervention to optimize any functional gains that follow chemodenervation induced by BoNTA has not been established. METHOD: One individual with chronic stroke who received BoNTA injections for upper and lower extremity spasticity was included. Physical therapy intervention consisted of 45- to 60-min sessions twice weekly for 12 weeks, based on the Bobath-neurodevelopmental therapy approach, and an activity-based home program. RESULTS: After BoNTA injections and physical therapy, the patient made clinically significant improvements in balance and gait speed and became more independent with his ambulation. CONCLUSIONS: This case report demonstrates that physical therapy after BoNTA injections can result in significant functional improvements for individuals with spasticity after chronic stroke that may not be possible with BoNTA injections alone.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".