A Multicentre Study: The Use of Micrografts in the Reconstruction of Full-Thickness Posttraumatic Skin Defects of the Limbs—A Whole Innovative Concept in Regenerative Surgery
Bibliographic record
Abstract
The skin graft is a surgical technique commonly used in the reconstructive surgery of the limbs, in order to repair skin loss, as well as to repair the donor area of the flaps and cover the dermal substitutes after engraftment. The unavoidable side effect of this technique consists of unaesthetic scars. In order to achieve the healing of posttraumatic ulcers by means of tissue regeneration and to avoid excessive scarring, a new innovative technology based on the application of autologous micrografts, obtained by Rigenera technology, was reported. This technology was able to induce tissue repair by highly viable skin micrografts of 80 micron size achieved by a mechanical disaggregation method. The specific cell population of these micrografts includes progenitor cells, which in association with the fragment of the Extracellular Matrix (ECM) and growth factors derived by patients' own tissue initiate biological processes of regeneration enhancing the wound healing process. We have used this technique in 70 cases of traumatic wounds of the lower and upper limbs, characterized by extensive loss of skin substance and soft tissue. In all cases, we have applied the Rigenera protocol using skin micrografts, achieving in 69 cases the complete healing of wounds in a period between 35 and 84 days. For each patient, the reconstructive outcome was evaluated weekly to assess the efficacy of this technique and any arising complication. A visual analogue scale (VAS) was administered to assess the amount of pain felt after the micrografts' application, whereas we evaluated the scars according to the Vancouver scale and the wound prognosis according to Wound Bed Score. We have thus been able to demonstrate that Rigenera procedure is very effective in stimulating skin regeneration, while reducing the outcome scar.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".