Outcomes of the Southampton Wound Grading System in Punch Biopsy Wounds
Bibliographic record
Abstract
Introduction: Skin biopsy punch is a common instrument used by dermatologists in day-to-day practice. The use of this device is associated with minimal complications. However, we cannot deny the possibility of adverse effects. We aimed to grade and track therapeutic skin punch biopsy wounds over 3 months using the Southampton Wound Grading System (SWGS). Materials and Methods: Selected patients in whom skin punch biopsy could be used therapeutically were enrolled. The procedures were performed under local anesthesia with suturing whenever needed. Immediate complications were noted, with follow-ups after 1 week, 1 month, and 3 months. The complications were noted, and the wounds were graded. Results: A total of 56 lesions from 45 patients underwent five procedures (punch excision, narrow hole extrusion technique, pinch-punch excision, enucleation of corn, and punch grafting). The most common immediate complication was a dog-ear defect; surgical wound dehiscence predominated at one week and one month, while post-inflammatory hyperpigmentation and hypertrophic scars were most common at three months. SWGS grades after one week were IC (67.86%), 0 (17.86%), IA (12.50%), and IIC (1.78%); after one month, 0 (64.29%) and IC (35.71%); after three months, 0 (76.79%) and IC (23.21%). Inferential analysis showed that larger punch sizes were associated with higher complication rates (p=0.049), and sutured wounds had more complications than non-sutured wounds (p=0.0028). Conclusion: There were predominantly minor complications like dog-ear defect and mild erythema, with no postoperative infections. Larger punch sizes and suturing were associated with more complications, underscoring careful punch selection and closure technique.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".