<p>Topical 5 fluorouracil cream versus combined 5 flurorouracil and fractional erbium YAG laser for treatment of severe hypertrophic scars</p>
Bibliographic record
Abstract
BACKGROUND: Topical 5 fluorouracil (5-FU) has been reported as one of the standard treatments for hypertrophic scars (HTS). Ablative fractional laser was found to have promising results in the delivery of topical drugs into the skin by creating vertical channels through which the drugs can penetrate the skin. So far there have been no comparative studies performed to compare both modalities in the same patient and same anatomical region, especially in severe HTS. OBJECTIVE: The aim of this study was to compare the effectiveness of topical 5-FU and combined topical 5-FU and laser in treating severe HTS. PATIENTS AND METHODS: Twenty-four severe HTS lesions were treated by 5-FU monotherapy and 5-FU combined with ablative fractional erbium YAG laser. Each lesion was divided into two parts. One part was treated with topical 5-FU twice weekly for 8 months. The other part was treated with combined topical 5-FU and ablative fractional erbium YAG laser once per month for 8 months. The scars' improvement was evaluated by Vancouver scar scale (VSS) and skin analysis camera. RESULTS: The assessment by VSS showed a significant reduction in the mean height, pliability, and vascularity of the lesions which were treated with combined approaches compared to 5-FU monotherapy. Pain and ulceration occurred at a higher rate in the combination therapy group. CONCLUSION: Treatment of severe HTS with combined 5-FU and ablative fractional erbium YAG laser is more effective than 5-FU alone.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".