Evaluation of the Effect of Cell Spray Technique in the Treatment of Partial-thickness Skin Loss.
Bibliographic record
Abstract
Introduction: Early excision and prompt resurfacing with skin grafts is the mainstay of surgical treatment of extensive raw areas. However, early excision with autograft coverage may be difficult to be achieved in patients with extensive burns due to the limited size of donor sites. Aim of the work:The aim of this study is to evaluate the treatment of post-burn and post-traumatic raw areas using Cell Spray Technique to better understand its indication at a maximum benefit. Patients and methods: 20 Patients aged from 2-40 years old with a partial-thickness to deep partial-thickness wound of up to 20% total body surface area (TBSA) requiring surgical debridement and skin grafting participated in this study, underwent cell spray-on grafting on their raw are after harvesting a normal graft from their donor site, separate dermis from epidermis, mince the epidermis and put it in a suspension solution of ringer Lactate. Results: Majority of patient rejected the sprayed cells or showed subsequent scars that scored high on the Vancouver Scar Score. Discussion: We tried to apply the same clinical technique of the ReCell device in our study, to best achieve the benefits of minimizing the donor site to cover up larger recipient defects in an economic way, by substituting every tool included in the ReCell device kit with a cheaper tool from our operating field, trying to maintain the level of quality of the healing process and the resultant scar outcome. We tried to assess the quality of healing process and the resulting scar, and its relation to every factor we managed in the criteria and to figure out how far these elements affected the results of our study. We recommend: Further trials of the technique to achieve higher healing quality, and thorough selection of the candidate patients to avoid factors that disrupt the healing process and worsen the resultant 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".