A New Hydrocellular Wound Dressing Used After Dermatologic Surgery: Dermatologic Surgeons' and Patients' Perceptions
Bibliographic record
Abstract
BACKGROUND: Optimum postsurgical healing requires appropriate dressing use. OBJECTIVE: This study assessed effectiveness and tolerance of a novel, hydrocellular dressing in dermatologic surgery using validated tools, describing its use in clinical practice, and comparing surgeons' and patients' perceptions of scar evolution. METHODS: This study examined direct suture closures of surgical excisions of small- to medium-sized skin lesions on the extremities or trunk. Dressings were changed 3 times/week. The Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessment Scales (POSAS) were used to assess outcomes. Data were collected at Day 0 (D0, FLC application), Day 15 to 21 (D15-21, suture removal), and Day 45 (D45) postprocedure by the surgeon and the patient. RESULTS: There were 128 patients (mean age: 55.1 years, 56.1% women). Mean length and width of the excisions were 3.5 × 1.65 cm and the most common FLC applied was 8 × 8 cm (67.7%). Most scars had normal pigmentation, pliability, and height at D15 to 21 and D45, as reported by patients and surgeons using VSS. Patient scores on visual analog scale (VAS) were high (>8/10) and global satisfaction measured by POSAS was generally high (>7/10 at D15-21; >8/10 at D45). CONCLUSION: These dressings were effective in managing surgical excisions, as assessed by VSS, VAS, and POSAS. Further controlled studies investigating various dressings in wound repair are needed.
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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 | 0.002 | 0.005 |
| 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".