Surgical Scar Management and Outcomes in Racial/Ethnic Minorities: A Systematic Review
Bibliographic record
Abstract
Background: Patients with darker skin tend to experience an increased prevalence of adverse surgical scarring and poorer treatment response in comparison to White patients. Ethnic and racial factors play a role in overall surgical scar outcomes because they predispose darker-skinned individuals to sequelae such as scar hypertrophy, keloid formation, and an overall negative psychosocial impact. This systematic review will summarize existing literature on surgical scar outcomes and management in minority patients and will highlight gaps in the medical literature. Methods: The search was conducted using PubMed, Embase, Scopus, and Cochrane Library to identify relevant articles. All articles went through title and abstract screening, followed by full-text review. Results: Of 1235 articles, 40 met eligibility criteria. Following the full-text review, 10 articles were included. In 5 of the 10 studies, patients were characterized as having Fitzpatrick skin types II–V. Five studies utilized laser techniques, and the remaining 3 studies utilized silicone sheet, topical silicone, and surgery. The Vancouver Scar Scale was the most utilized assessment tool. The two studies that evaluated fractional CO2 laser interventions using the Vancouver Scar Scale showed improvement in scar outcomes and overall patient satisfaction. Conclusions: Laser interventions were the most utilized and show promise for improving scar management outcomes in ethnic patients, though there is little work highlighting treatment decision-making in scar management. This review emphasizes the need for increased research focused on scar management interventions and comprehensive protocols to address scar management in plastic surgery for patients with darker skin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".