Utility of Hyaluronidase in Combination with Triamcinolone Acetonide and 5-Fluorouracil in Treatment of Hypertrophic Scars and Keloids: A Comparative Study
Bibliographic record
Abstract
Background: Keloids and hypertrophic scars are a commonly encountered problem in dermatology. There are many treatment modalities available with variable efficacy and recurrences. This study highlights the use of hyaluronidase with triamcinolone acetonide (TAC) and 5-fluorouracil (5-FU). Purpose: To assess the utility of hyaluronidase in combination with TAC and 5-FU in treatment of hypertrophic scars and keloids. Methods: In this study a combination of hyaluronidase along with TAC and 5-FU was given in one part and a combination of TAC and 5-FU was given in another half of same hypertrophic scar/keloid keeping 1cm gap in between the two untreated at 4 weekly interval for 24 weeks. Results were assessed by measuring height, volume of lesion and their percentage decrease. Also, pliability, vascularity, pigmentation of lesion and Vancouver scar assessment scale score were assessed. Side effects like ulceration, surrounding skin atrophy and telangiectasias were noted. Results: There was a reduction in all the parameters in both the treatment segments. Faster improvement in height, volume, pliability, vascularity, pigmentation, and Vancouver scar assessment scale score was noted with combination of hyaluronidase, TAC, and 5-FU compared to TAC and 5-FU alone. Side effect profile of ulceration, surrounding skin atrophy and telangiectasias was comparable in both the segments. Conclusion: Combination of hyaluronidase along with TAC and 5-FU offered a better outcome and faster response when compared to combination of TAC and 5-FU.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".