331 The Power of Yoga: Clinical Outcomes and Cutaneous Functional Unit Recruitment for a Patient with Cervical and Upper Extremity Burn Scar Contracture
Bibliographic record
Abstract
Burn scar contracture greatly limits function and participation for burn survivors, especially if the scarring crosses multiple joints. Previous research identified fields of skin recruited during single joint motion, cutaneous functional units (CFU), that indicate movement impairments may be seen distal to the injured tissue. This case report seeks to connect the principles of CFU and specific yoga poses in improving clinical outcomes for a burn survivor. The selected patient is a 38-year-old male who sustained deep electrical burns to his neck, chest and bilateral upper extremities, presenting with significantly decreased range of motion (ROM) that limit his functional independence. The patient attended physical therapy 4-days a week where he performed a specific yoga program including Child’s pose, Fish pose, and Camel pose during each session. Outcome measures including ROM measures, the Vancouver scar scale (VSS), and the neck disability index (NDI) were recorded every 10 sessions. CFUs of cervical extension, shoulder flexion, and elbow extension were analyzed via photographs comparing cutaneous position during a specified yoga pose, and resting anatomical position. Over 30 visits, cervical and shoulder ROM increased, though 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 non-burned skin in positions of stretch during selected yoga poses. The results of this case report provide preliminary evidence that stretching exercises involving multiple joints (ie. Yoga) may recruit more CFUs than targeted single-joint stretches, which may facilitate significant clinical outcomes related to an increase in joint range of motion. Yoga poses involving multiple joints fall in line with the principle of CFUs, warranting continued investigation. Incorporating yoga poses specifically selected for individual patients appear to contribute to improved clinical range of motion outcomes when paired with traditional burn rehabilitation interventions. Considering the identified CFUs when designing stretching programs may contribute to improved clinical outcomes for burn survivors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".