Effective keloid management using a combinatorial continuous‐wave and repeat fractionated ablative <scp>CO<sub>2</sub></scp> laser regimen
Bibliographic record
Abstract
BACKGROUND: Keloids are benign proliferative scars that form as a result of dysregulated growth and collagen deposition in response to cutaneous injury. Laser therapies have emerged as promising options for the treatment of keloids, with performance varying by laser type and lesion characteristics. PURPOSE: laser treatment of keloids. METHODS: laser modes. A single session of continuous wave followed by five sessions of fractional delivery. Efficacy was assessed using the Patient and Observer Scar Assessment Scale (POSAS) and the Vancouver Scar Scale. The Numeric Rating Scale was used to assess patient satisfaction and pain. RESULTS: Most patients were female (77.3%) with skin type IV (72.7%), age was 24.3 ± 9.3 years, most keloids were located on the earlobe (56.5%) or arm or hand (17.4%), size ranged from 5 to 10 cm, and time since injury ranged from 3 months to 35 years. No serious adverse events were reported. At 6 months, significant improvements from baseline occurred in all characteristics, scar color (4.8 ± 2.8 to 1.9 ± 1.1), rigidity (5.0 ± 2.8 vs. 5.4 ± 2.8), thickness (5.4 ± 2.8 vs. 2.0 ± 1.1), and irregularity (5.9 ± 2.4 vs. 1.9 ± 0.9). The Vancouver scores followed a similar trend. Patient-rated overall improvement from 37 ± 17.6 at baseline to 16.1 ± 8.5 at 6 months, and improvement in associated pain and pruritus. CONCLUSION: Combination of two ablative laser delivery modes within a single laser platform provided for effective and safe keloid management and left patients highly satisfied.
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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".