A Translational Animal Model for Scar Compression Therapy Using an Automated Pressure Delivery System.
Bibliographic record
Abstract
BACKGROUND: Pressure therapy has been used to prevent and treat hypertrophic scars following cutaneous injury despite the limited understanding of its mechanism of action and lack of established animal model to optimize its usage. OBJECTIVES: The aim of this work was to test and characterize a novel automated pressure delivery system designed to deliver steady and controllable pressure in a red Duroc swine hypertrophic scar model. METHODS: Excisional wounds were created by dermatome on 6 red Duroc pigs and allowed to scar while assessed weekly via gross visual inspection, laser Doppler imaging, and biopsy. A portable novel automated pressure delivery system was mounted on developing scars (n = 6) for 2 weeks. RESULTS: The device maintained a pressure range of 30 ± 4 mm Hg for more than 90% of the 2-week treatment period. Pressure readings outside this designated range were attributed to normal animal behavior and responses to healing progression. Gross scar examination by the Vancouver Scar Scale showed significant and sustained (>4 weeks) improvement in pressure-treated scars (P < .05). Histological examination of pressure-treated scars showed a significant decrease in dermal thickness compared with other groups (P < .05). Pressure-treated scars also showed increased perfusion by laser Doppler imaging during the treatment period compared with sham-treated and untreated scars (P < .05). Cellular quantification showed differential changes among treatment groups. CONCLUSION: These results illustrate the applications of this technology in hypertrophic scar Duroc swine model and the evaluation and optimization of pressure therapy in wound-healing and hypertrophic scar management.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".