An Intensive Programme of Passive Stretch and Motor Training to Manage Severe Knee Contractures after Traumatic Brain Injury: A Case Report
Bibliographic record
Abstract
PURPOSE: While contemporary management of contractures (a common secondary problem of acquired brain injury that can be difficult to treat) includes passive stretch, recent evidence indicates that this intervention may not be effective. This may be because clinical trials have not provided a sufficient dose or have not combined passive stretch with other treatments. The purpose of this case report is to describe a programme of intensive passive stretch combined with motor training administered over a 1.5-year period to treat severe knee contractures. METHOD: Five months after traumatic brain injury, an adolescent client with severe contractures in multiple joints underwent an intensive stretch programme for his knee contractures, including serial casting and splinting, which was administered for 10 months in conjunction with a motor training programme administered for 1.5 years. RESULTS: The client regained full extension range in his knees and progressed from being totally dependent to walking short distances with assistance; these effects were maintained at follow-up 5.5 years after injury. CONCLUSION: The use of a high dose of passive stretch in conjunction with motor training may be an option to consider for correcting severe contractures following acquired brain injury.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| 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".