Modified Meek Micrografting Technique for Wound Coverage in Extensive Burn Injuries
Bibliographic record
Abstract
The modified Meek micrografting technique constitutes a rapid and efficient surgical approach for the skin coverage of extensive full-thickness burn injuries. A total of 10 burn patients (mean 68 ± 9.2% TBSA) admitted to our burn unit required one or more Meek micrografting procedures (mean 2.2 ± 0.5) to cover in average 43.4 ± 11.6% TBSA (range between 10 and 75% TBSA). This goal was achieved using a donor site area ranging between 2.5 and 18% TBSA. All patients developed local infection to Pseudomona aeruginosa (75%), Stenotrophomona maltophilia (25%), methicillin-resistant Staphylococcus aureus (12.5%), and Acinetobacter baumannii (12.5%). Thus, the average of Meek regrafting after graft-take failure was 13.1 ± 6.4% TBSA (median: 9%; range from 0 to 36%). The period to obtain stable definitive wound closure was in average of 67.2 ± 21 days post injury. The modified Meek micrografting provides a reliable and versatile method for the coverage of large burn wounds with limited autograft donor sites and is now routinely used in our institution. Its systematic use improves operating times and overall outcomes reducing the number of surgeries, increasing the percentage of graft take, and decreasing the length of stay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.002 | 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".