Integra Dermal Regeneration Template in Reconstruction of Primary Oncologic Defects in the Lower Extremities: A Case Series
Bibliographic record
Abstract
This article presents a case series of 4 patients who underwent primary reconstruction of oncologic defects in the lower extremities using Integra Dermal Regeneration Template (IDRT). The patients had either primary or recurrent tumors, which resulted in exposure of deep underlying structures including tendon, nerve, muscle, and bone. IDRT was selected to manage these defects due to the uncertain malignant potential of tissue margins and its ability to facilitate later margin revision without sacrificing tissue. The use of IDRT allowed for the growth of a neodermis that supported subsequent split-thickness skin grafting in all cases. Additionally, for those with positive margins, surgical revision and skin graft application was able to be performed in a single procedure, maximizing operating room use and patient convenience. This case series highlights the potential of IDRT in managing complex oncologic defects in the lower extremities, expanding options for reconstructive surgeons. Key findings: (1) IDRT is a viable option for reconstruction in oncologic resections exposing deep structures. (2) In cases with unknown malignant potential of tissue margins, use of IDRT can allow for revision of positive margins without sacrificing graft or flap tissue. (3) Negative pressure wound therapy is an important adjunct in achieving a favorable neodermis for acceptance of a spilt thickness skin graft.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".