Scalp Reconstruction using Dermal Induction Template: State of the Art and Personal Experience.
Bibliographic record
Abstract
The loss of skin envelope is a frequent and costly problem in health care. This article provides an overview on the state of the art in scalp reconstruction with dermal substitutes, as well as our personal experience of ten critical patients with non-melanoma skin cancer of the scalp. These patients were treated in a two-stage procedure by wide tumor excision, apposition of a dermal induction template (Hyalomatrix (R)) and successive skin grafting. Four patients underwent subgaleal tumor excision with preservation of the periosteum and six patients en bloc tumor excision together with the external cortical bone. A 10x10 cm template was used in all patients. Two weeks after demolition surgery, we observed neodermis formation. Results were documented by comparative photography, visual analogue scale for patient satisfaction, and Vancouver scar scale for evaluation of final graft characteristics. Patients were tumor-free during follow-up. The procedure achieved good scalp reshaping and graft scarring evolution. Patient satisfaction was high. Hyalomatrix (R) was effective for oncological scalp reconstruction in critical patients. It prepared the wound bed for graft take while awaiting histological diagnosis and confirmation of margin clearance. Further studies on dermal substitutes are needed to improve benefit in patients.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".