The Role of Massage in Scar Management: A Literature Review
Bibliographic record
Abstract
BACKGROUND: Many surgeons recommend postoperative scar massage to improve aesthetic outcome, although scar massage regimens vary greatly. OBJECTIVE: To review the regimens and efficacy of scar massage. METHODS: PubMed was searched using the following key words: "massage" in combination with "scar," or "linear," "hypertrophic," "keloid," "diasta*," "atrophic." Information on study type, scar type, number of patients, scar location, time to onset of massage therapy, treatment protocol, treatment duration, outcomes measured, and response to treatment was tabulated. RESULTS: Ten publications including 144 patients who received scar massage were examined in this review. Time to treatment onset ranged from after suture removal to longer than 2 years. Treatment protocols ranged from 10 minutes twice daily to 30 minutes twice weekly. Treatment duration varied from one treatment to 6 months. Overall, 65 patients (45.7%) experienced clinical improvement based on Patient Observer Scar Assessment Scale score, Vancouver Scar Scale score, range of motion, pruritus, pain, mood, depression, or anxiety. Of 30 surgical scars treated with massage, 27 (90%) had improved appearance or Patient Observer Scar Assessment Scale score. CONCLUSIONS: The evidence for the use of scar massage is weak, regimens used are varied, and outcomes measured are neither standardized nor reliably objective, although its efficacy appears to be greater in postsurgical scars than traumatic or postburn scars. Although scar massage is anecdotally effective, there is scarce scientific data in the literature to support it.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".