Use of cadaveric skin allograft and Integra Dermal Regeneration Template to manage deep lower limb injuries
Bibliographic record
Abstract
OBJECTIVE: In full-thickness wounds, it is necessary to have an appropriate dermal replacement because dermal tissue does not regenerate into normal dermis after injury. The use of a dermal matrix underneath a skin graft during the healing process provides a scaffold that supports tissue growth, resulting in improvement of cosmesis and functional outcomes. The management of large wounds with deep skin impairment using a combination of dermal matrices has not been exhaustively studied. The objective of this study was to evaluate the results of managing lower limb trauma, with deep skin impairment, by combining the use of dermal matrices in stages. METHOD: This was a retrospective study of patients with lower limb trauma managed using a combination of cadaveric skin and Integra Dermal Regeneration Template (IDRT; Integra LifeSciences Corp., US) in stages, followed by an autologous skin graft, in the Hospital Aleman, Buenos Aires, Argentina from 2014-2021. Cosmesis was evaluated with the Vancouver Scar Scale (VSS) and Patient and Observer Scar Assessment Scale (POSAS). Functional outcomes were assessed one year after surgery. RESULTS: In total, five patients were treated. The average affected body surface area was 11.2%. The average cadaveric skin, IDRT and skin autograft take rates were 98.4%, 98.4% and 99%, respectively. Upon follow-up, six months after surgery, the mean VSS was 3.2 and the mean POSAS was 27.8. After 12 months, the mean VSS was 2.6 and the mean POSAS was 22.6. In addition, no depression of the covered surfaces was observed. All patients recovered full articular function and movement after physical therapy. CONCLUSION: All patients presented full wound coverage with satisfactory cosmesis and functional outcomes. The combination of the use of cadaveric skin and IDRT consecutively in the same wound bed provides promising results for the management of lower limb trauma wounds with deep skin impairment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".