Early postoperative treatment of mastectomy scars using a fractional carbon dioxide laser: a randomized, controlled, split-scar, blinded study
Bibliographic record
Abstract
BACKGROUND: Mastectomy leaves unsightly scarring, which can be distressing to patients. Laser therapy for scar prevention has been consistently emphasized in recent studies showing that several types of lasers, including fractional ablation lasers, are effective for reducing scar formation. Nonetheless, there are few studies evaluating the therapeutic efficacy of ablative CO2 fractional lasers (ACFLs). METHODS: This study had a randomized, comparative, prospective, split-scar design with blinded evaluation of mastectomy scars. Fifteen patients with mastectomy scars were treated using an ACFL. Half of each scar was randomized to "A," while the other side was allocated to group "B." Laser treatment was conducted randomly. Scars were assessed using digital photographs of the scar and Vancouver scar scale (VSS) scores. Histological assessments were also done. RESULTS: The mean VSS scores were 2.20±1.28 for the treatment side and 2.96±1.40 for the control side. There was a significant difference in the VSS score between the treatment side and the control side (P=0.002). The mean visual analog scale (VAS) scores were 4.13±1.36 for the treatment side and 4.67±1.53 for the control side. There was a significant difference in VAS score between the treatment side and the control side (P=0.02). CONCLUSIONS: This study demonstrated that early scar treatment using an ACFL significantly improved the clinical results of the treatment compared to the untreated scar, and this difference was associated with patient satisfaction.
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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".