Negative Pressure Wound Therapy for a Giant Wound Secondary to Malignancy-induced Necrotizing Fasciitis: Case Report and Review of the Literature.
Bibliographic record
Abstract
BACKGROUND: Necrotizing fasciitis (NF) is a life-threatening condition in which rapid diagnosis, debridement of nonviable tissue, and broad-spectrum antibiotics are critical to effective treatment. The debridement required can be extensive, resulting in large wounds that can sometimes be covered with split-thickness skin grafts (STSGs) with the help of negative pressure wound therapy (NPWT), or vacuum-assisted closure, to decrease the wound size. CASE REPORT: The authors report a rare case of NF due to malignancy-associated bowel perforation with a giant lower extremity wound secondary to debridement that involved 20% of the total body surface area (TBSA) in a 64-year-old, previously healthy, nonsmoking man. The wound was surgically debrided twice and packed before NPWT was applied. Based on the authors' literature search, this case is 1 of the single largest wounds successfully managed with a STSG and NPWT. CONCLUSIONS: Rapid diagnosis of NF is critical to guide surgical management and administration of antibiotics. It is important to be mindful of the origin of certain necrotizing infections, and clinicians should have a greater index of suspicion for NF when assessing skin infections in unwell patients with concomitant bowel perforation secondary to gastrointestinal malignancy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".