Reconstructive Surgery with Integra Dermal Regeneration Template: Histologic Study, Clinical Evaluation, and Current Practice
Bibliographic record
Abstract
BACKGROUND: Yannas and Burke developed the concept of the dermal regeneration template in the 1970s. It is now a widely accepted tool in the treatment of burns as well as in reconstructive surgery. METHODS: The authors present a previously published study of Integra used in 20 consecutive patients to reconstruct 30 anatomical sites and then analyze the histologic and clinical outcomes. Wound healing was evaluated by examination of weekly punch biopsy specimens with standard and immunohistochemical stains. Patient satisfaction was assessed using a visual analogue scale, and scar appearance was assessed using a modified Vancouver Scar Scale. RESULTS: Four distinct phases of dermal regeneration could be demonstrated histologically: imbibition, fibroblast migration, neovascularization, and remodeling and maturation. Full vascularization of the neodermis occurred at 4 weeks. Patients reported increased range of movement and improvement in appearance compared with their preoperative states. CONCLUSIONS: The color of the matrix reflected the stage of neodermal vascularization. No adnexa, nerve endings, or elastic fibers were seen in any of the specimens. The new collagen was histologically indistinguishable from normal dermal collagen. The authors also present their current protocol and experience with using Integra for a range of reconstructive procedures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".