Optimizing Postsurgical Scars: A Systematic Review on Best Practices in Preventative Scar Management
Bibliographic record
Abstract
BACKGROUND: Scar management is critical for every plastic surgeon's practice and, ultimately, the patient's satisfaction with his or her aesthetic result. Despite the critical nature of this component of routine postoperative care, there has yet to be a comprehensive analysis of the available literature over the past decade to assess the best algorithmic approach to scar care. To this end, a systematic review of best practices in preventative scar management was conducted to elucidate the highest level of evidence available on this subject to date. METHODS: A computerized MEDLINE search was performed for clinical studies addressing scar management. The resulting publications were screened randomized clinical trials that met the authors' specified inclusion/exclusion criteria. RESULTS: This systematic review was performed in May of 2016. The initial search for the Medical Subject Headings term "cicatrix" and modifiers "therapy, radiotherapy, surgery, drug therapy, prevention, and control" yielded 13,101 initial articles. Applying the authors' inclusion/exclusion criteria resulted in 12 relevant articles. All included articles are randomized, controlled, clinical trials. CONCLUSIONS: Optimal scar care requires taking into account factors such as incisional tension, anatomical location, and Fitzpatrick skin type. The authors present a streamlined algorithm for scar prophylaxis based on contemporary level I and II evidence to guide clinical practice.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".