Yoga and Cutaneous Functional Unit Recruitment for a Patient with Cervical and Upper Extremity Burn Scar Contracture: Case Report
Bibliographic record
Abstract
Burn scar contracture greatly limits function for burn survivors, particularly when the scarring crosses multiple joints. Previous research has identified fields of skin recruited during single joint motion, called cutaneous functional units (CFU), indicating that impairments may be seen distal to the injured tissue. This case report connects the principles of CFU and yoga-inspired therapy modalities in improving clinical outcomes for a burn survivor. The patient is a 38-year-old male who sustained deep partial-thickness electrical burns to his neck, chest, and bilateral upper extremities, presenting with significantly decreased range of motion. The patient attended physical therapy 4 days a week, where he performed a specific yoga asana program during each session. Outcomes including standard range of motion measures, the Vancouver Scar Scale (VSS), and the Neck Disability Index (NDI), which were recorded every 10 sessions. CFUs of cervical extension and shoulder flexion were analyzed via photographs comparing cutaneous position during specified yoga poses and resting anatomical position in standing. Over 30 visits, cervical and shoulder range of motion increased, although the VSS and NDI did not show significant improvement. Yoga poses showed overall cutaneous recruitment distal to the targeted joints, and burned skin was recruited similarly to nonburned skin in positions of stretch. Incorporating multijoint approaches for stretching, like yoga, appears to contribute to improved clinical range-of-motion outcomes when paired with traditional burn-rehabilitation interventions. Yoga poses involving multiple joints align with the principle of CFUs, warranting continued investigation.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".