Autologous bilayered self-assembled skin substitutes (SASSs) as permanent grafts: a case series of 14 severely burned patients indicating clinical effectiveness
Bibliographic record
Abstract
Split-thickness skin autografts (AGs) are the standard surgical treatment for severe burn injuries. However, the treatment of patients with substantial skin loss is limited by the availability of donor sites for skin harvesting. As an alternative to skin autografts, our research group developed autologous self-assembled skin substitutes (SASSs), allowing the replacement of both dermis and epidermis in a single surgical procedure. The aim of the study was to assess the clinical outcome of the SASSs as a permanent coverage for full-thickness burn wounds. Patients were recruited through the Health Canada's Special Access Program. SASSs were grafted on debrided full-thickness wounds according to similar protocols used for AGs. The graft-take and the persistence of the SASS epithelium over time were evaluated. 14 patients received surgical care with SASSs. The mean percentage of the SASS graft-take was 98 % (standard deviation = 5) at 5 to 7 d after surgery. SASS integrity persisted over time (average follow-up time: 3.2 years), without noticeable deficiency in epidermal regeneration. Assessment of scar quality (skin elasticity, erythema, thickness) was performed on a subset of patients. Non-homogeneous pigmentation was noticed in several patients. These results indicated that the SASS allowed the successful coverage of full-thickness burns given its high graft-take, aesthetic outcome equivalent to autografting and the promotion of long-term tissue regeneration. When skin donor sites are in short supply, SASSs could be a valuable alternative to treat patients with full-thickness burns covering more than 50 % of their total body surface area.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".