A Pilot Study of Microcolumn Skin Grafting in Full-thickness Burns
Bibliographic record
Abstract
This pilot study evaluated the feasibility of treating third-degree, full-thickness burn wounds with both split-thickness skin grafts (STSGs) and micro skin tissue columns (MSTC). Donor sites for both grafting techniques were also assessed. Patients aged ≥18 years with ≤60% TBSA third-degree, full-thickness burns were enrolled. One 2.5 × 2.5 cm2 wound area was treated in each subject, with the remaining portion of the wound used as an internal control. The target wound was treated with MSTCs + STSG while the control site was treated with STSG. Patients were followed for up to 9 months after wound closure. Primary endpoints included re-epithelialization rate, scarring (VSS, Patient and Observer Scar Assessment Scale), and donor site pain (visual analogue scale). Ten patients were enrolled. Overall, MSTC donor sites were less painful, epithelialized faster, and resulted in improved Patient Observer Scar Assessment Scale and Vancouver Scar Scale (VSS) scores than STSG donor sites. For all endpoints, there were no differences in the recipient wounds grafted with or without MSTCs. Intraoperative MSTC grafting is feasible and results in minimal donor site morbidity. This pilot study was unable to demonstrate enhanced wound healing or reduced scar formation when MSTCs were applied simultaneously with STSGs to burn wounds. Larger clinical studies are needed to assess the utility of MSTCs in conjunction with STSGs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".