Evaluation of the Efficacy of Laser Therapy in Treating Postoperative Scars in Athletes and Its Impact on Recovery Time and Return to Physical Activity
Bibliographic record
Abstract
Postoperative scarring presents a significant challenge in sports medicine, particularly for athletes seeking a rapid return to intensive physical activity. This study aimed to evaluate the efficacy of laser therapy in improving the quality of postoperative scars and its impact on rehabilitation time. A cohort of competitive athletes aged 18–40 with hypertrophic scars resulting from orthopedic surgeries underwent five biweekly sessions of laser therapy using fractional CO2 and pulsed dye lasers (PDL). Scar assessment included the Vancouver Scar Scale (VSS) and Cutometer-based skin elasticity measurements, with additional evaluations of pain and functionality using the Visual Analog Scale (VAS). Fractional CO2 laser therapy significantly reduced VSS scores by 2.5 points and increased skin elasticity by 15%, while PDL therapy effectively decreased erythema by 30%. Athletes treated with laser therapy returned to high-intensity training an average of three weeks earlier than those receiving standard scar treatments. Side effects, such as transient redness and swelling, were mild and resolved within 2–3 days. These findings suggest that laser therapy, particularly fractional CO2 and PDL, is a safe and effective intervention for managing postoperative scars in athletes, enhancing both aesthetic and functional outcomes while accelerating rehabilitation timelines. Further research is recommended to optimize protocols and evaluate long-term benefits.
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 | 0.002 | 0.002 |
| 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".