Negative Pressure Wound Therapy: An Adjunct in Reconstructing Large Periocular Defects From Necrotizing Fasciitis
Bibliographic record
Abstract
PURPOSE: To demonstrate the use of negative pressure wound therapy (NPWT) and other reconstructive techniques in the reconstruction of large tissue defects resulting from periocular necrotizing fasciitis (NF). METHODS: Description of technique with 3 illustrative cases and accompanying photographic montage. RESULTS: Technique : Debridement successfully spared post-septal tissues and the lid margin in all cases. Wounds were left open for several days until progressive infection was ruled out. NPWT was applied using hydrophobic, compressible open-pore polyurethane foam, and high-tensile polyvinyl alcohol foam to bolster the tarsorrhaphized eyelid and prevent exposure. A polyurethane, acrylic adhesive was applied overtop, and continuous vacuum at -75 mm Hg via a NPWT unit was applied for 4 to 14 days. Cases of NF included a 65-year-old woman with metastatic lung cancer, a 59-year-old man with diabetes, and a 57-year-old man with progressive preseptal cellulitis. In all cases, NPWT resulted in impressive macrodeformation and granulation of wounds, facilitating further repair using skin grafts, temporalis and orbicularis muscle flaps, and biodegradable temporizing matrix. Considering the initial extent of these wounds, all patients had excellent functional and aesthetic outcomes at 6 to 9 months, and no ophthalmic complications were observed. CONCLUSIONS: In this series, NPWT was safe and effective in facilitating complex reconstruction of large periocular tissue defects resulting from necrotizing fasciitis.
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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) | 0.000 | 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 | 0.000 |
| 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".