Laser‐assisted topical steroid application versus steroid injection for treating keloids: A split side study
Bibliographic record
Abstract
BACKGROUND: Approaches to improve keloid scars include intralesional corticosteroid injections and fractional lasers exclusively. The combinative use of ablative fractional laser therapy and occluded topical corticosteroid as a drug delivery method enhances therapeutic outcome of two efficient scar therapy modules into one simple synergistic module. AIM: To compare the therapeutic effect of combining two modalities of scar treatment, the first is fractional ablative laser treatment and the other is occluded topical corticosteroid to the standard use of intralesional steroid injection. METHODS: Keloids from thirty suffering patients were split faced into two identical parts; one part received an intralesional corticosteroid injection while the other part was treated first with fractional ablative 2940 nm Er: YAG laser followed by occluded topical application of steroid cream. Four treatment sessions were performed with 4-week interval between sessions. Every session was assessed photographically and using the Vancouver Scar Scale (VSS). RESULTS: The mean keloid VSS before treatment was 6.9 ± 1.9. After treatment, the mean keloid VSS of the injection side became 2.63 ± 2.09, and mean keloid VSS of the laser-treated side became 2.07 ± 2.02. Each of the treated halves showed a statistically significant improvement in their VSS. However, no statistically significant differences were observed for either of the treated halves over the other one. CONCLUSION: Although intralesional steroids injection is the standard procedure for treatment of keloid scars, the use of ablative fractional laser-assisted delivery of topical steroid can offer a safer and a better aesthetic treatment option.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".