Reconstruction of large wounds using a combination of negative pressure wound therapy and punch grafting after excision of acral lentiginous melanoma on the foot
Bibliographic record
Abstract
Melanoma in darker-pigmented individuals often develops in an acral lentiginous fashion on the foot. After surgical removal of a tumor at this site, repair of the wound can be challenging. This is because there is an insufficient local skin pool and lack of mobility of the skin in this area. Moreover, functional aspects such as walking and weight bearing should be considered. We performed a combination treatment of negative pressure wound therapy (NPWT) and punch grafting on 15 patients, after wide excision of acral lentiginous melanomas on the foot, and compared these to 26 patients who underwent either secondary intention healing (SIH, n = 13) or NPWT (n = 13) alone. The punch grafting with NPWT group showed significantly shorter healing times than those of the other two groups. Evaluation of completely healed wounds using the Vancouver Burn Scar Assessment Scale revealed that the punch grafting group had mean values better, or comparable, to the SIH or NPWT group in four of the five scales (except pigmentation). As for complications, only one patient developed a wound infection after punch grafting. Further, by utilizing NPWT for fixation of punch grafts, it was possible to treat all subjects as outpatients after punch grafting. These results show that a combination treatment of NPWT and punch grafting is an excellent therapeutic option for post-wide excision wounds on the feet, with significantly shortened healing times and favorable cosmetic outcomes.
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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) | 0.001 | 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".