035 Evaluation of BiLayered Cell Therapy for Full Thickness Excision Wounds: A MultiCenter, Prospective, Randomized, Controlled Trial
Bibliographic record
Abstract
Management of surgical defects following excisional and Mohs micrographic surgery for malignant skin lesions consists of autografting, skin flaps, primary closure, and healing by secondary intention. A fully differentiated bilayered cell-based therapy (BLCT/Apligraf), expresses multiple growth factors, provides a biologically active matrix, and appears to be immunologically inert. BLCT is FDA approved for venous leg ulcers and diabetic foot ulcers. BLCT is effective in hard-to-heal venous ulcers and, in diabetic foot ulcers, it is significantly associated with less amputations and osteomyelitis. A previous open-label trial showed BLCT to be safe and effective in surgical excision wounds. However, we have been interested in rigorously identifying differences in chronic compared to acute wounds when using BLCT or other skin substitutes. In this report of a multicenter randomized study, we enrolled and treated 181 patients eligible for secondary intention healing after Mohs or excisional surgery for skin cancer, with 172 completing the study. Face wounds were excluded. In total, 84 patients were treated with BLCT and 88 with dressings alone. The primary efficacy endpoint was the quality of the healed wound using the Vancouver Burn Scar Assessment Scale (VBSAS). Pigmentation, vascularity, pliability, and scar heights were assessed by the investigator and an independent observer as 0 (no scar) to 15 (worst scar). Scores of ≤ 4 were assigned to 57 (65%) of BLCT-treated patients by both the investigator and observer, and to 60 (65%) and 54 (58%) of control patients according to the investigator and observer, respectively. There was no difference in healing time, and no severe adverse events or rapid cancer recurrence were observed in either group. In this randomized controlled trial of full thickness excision wounds after skin cancer removal, BLCT/Apligraf was safe and well tolerated.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".