A New Method in the Treatment of Postburn and Post-Traumatic Scar Contractures: Double-Opposing Z- and V- (K-M-N) Plasty
Bibliographic record
Abstract
BACKGROUND: To date, many techniques for the surgical treatment of postburn scar contractures have been described. Some of the most popular techniques are Z-plasty, V-Y-plasty and their analogues. A major limitation of these techniques is that the excess tissue requires excision of the dog ear. The current study presents a new modification of the double-opposing Z- and V-plasty, called 'K-M-N plasty'. METHODS: Twenty postburn scar contractures were successfully treated with K-M-N plasty. The postoperative results depict the versatility of this technique in the surgical treatment of postburn scar contractures, especially in the upper and lower extremities. RESULTS: There was no distal flap necrosis, and postoperative recovery was uneventful in all operated patients. K-M-N plasty is an effective and alternative method for the surgical treatment of postburn scar contractures. In addition, drawing and flap transpositions were not complicated. DISCUSSION: THERE ARE MANY ADVANTAGES TO USING THIS TECHNIQUE: K-M-N plasty can be safely used when skin tension crosses the contracture line; it is superior to other local flaps because of its rich vascularity and mobility for superficial scars; it can be recommended to the inexperienced surgeon because it can be performed with ease; it is also an effective procedure for the pericontracture area due to its V limb (it can prevent recontracture); the colour and texture matches are more cosmetically acceptable, and the resultant contracture release is similar to other techniques; the dog ear formation is not seen; it can be performed under local anesthesia in most cases (not in children); and it has a shorter period of operation and hospitalization than other techniques.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".