Wound Care With a Porcine Extracellular Matrix After Surgical Treatment of Rhinophyma
Bibliographic record
Abstract
Background Rhinophyma surgery is commonly associated with prolonged wound healing and the need for multiple wound dressings. Objectives To evaluate clinical outcome with a porcine extracellular matrix (ECM) after shave excision of rhinophyma compared with common wound care procedure. Materials and methods Retrospective analysis of patients with common dressings (CD) compared with patients with additional ECM (OASIS) application. Clinical findings were assessed prior to treatment and at follow-up visit using the Patient and Observer Scar Assessment Scale (POSAS), Vancouver Scar Scale (VSS), and Rhinophyma Severity Index (RHISI). Results Overall, 28 patients (67.5 ±9.0 years) with a mean wound area of 33.9 (±8.5) cm² were included. After a mean follow-up period of 132 (±73) days, scales of POSAS, VSS, and RHISI showed significant ( P< .0001) reductions of 47.0% (±11.1), 56.0% (±12.0), and 62.3% (±14.3), respectively. Subgroup analysis showed no significant differences of aforementioned parameters between the ECM group ( n= 17) and CD group ( n= 11). In contrast, the number of dressing changes were significantly ( P< .006) less in the ECM group (1.4 ±0.8) compared with CD group (4.1 ±2.6). The ECM group showed a significant ( P< .017) shorter time to re-epithelization (10.5 ±1.7 days) than the CD group (13.1 ±2.2 days). Conclusions The application of porcine ECM is practicable and reduces the number of dressing changes and time to re-epithelization clearly. Crusts are scaling off spontaneously without any aggressive action needed. Our findings indicate that ECM application is a promising approach for rhinophyma wound care.
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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".