Can Single Use Negative Pressure Wound Therapy be an alternative method to manage keloid scarring? A preliminary report of a clinical and ultrasound/colour‐power‐doppler study
Bibliographic record
Abstract
Keloid scarring represents a pathological healing where primary healing phenomenon is deviated from normal. Pico is a single use negative pressure wound therapy system originally introduced to manage open or just closed wounds. Pico dressing is made of silicone, and distributes an 80 mmHg negative pressure across wound bed. Combination of silicon layer and continuous compression could be a valid method to manage keloid scarring. Since November 2011, three patients were enrolled and evaluated before negative pressure treatment, at end of treatment (1 month) and 2 months later, through Vancouver Scar Scale (VSS), Visual Analog Scale (VAS) and a scoring system for itching. Ultrasound (US) and colour-power-doppler (CPD) examination was performed to evaluate thickness and vascularisation of the scar. One patient was discharged from study after 1 week. In last two patients, VSS, VAS and itching significantly improved after 1 month therapy and the results were stable after 2 months without any therapy. At end of therapy, the 'appearance of palisade vessels' disappeared in both cases at CPD exam; US showed a thickness reduction (average 43·8%). We propose a well-tolerated, non invasive treatment to manage keloid scarring. Prospective studies are necessary to investigate whether these preliminary observations are confirmed.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".