Intralesional Injection of Triamcinolone Acetonide Alone versus Triamcinolone Acetonide in Combination With 5-Flurouracil in Treatment of Keloid / A Randomised Comparative Clinical Study
Bibliographic record
Abstract
Background: Keloid is an abnormal fibrous tissue overgrowth that spreads beyond the original wound margins. In spite of the availability of multiple therapeutic modalities, keloid management is still a challenge for healthcare professionals due to the high recurrence rates. Aim: To evaluate the efficacy of intralesional injection (ILI) of triamcinolone acetonide (TAC) alone versus its combination with 5flurouracil (5FU) in treatment of keloid and to evaluate side effects of each modality. Patients and Methods: This study included 186 participants with keloid due to different causes. The cases were randomly assigned into two groups, group 1 (that included 93 patients received intralesional combination of TAC and 5FU) and group 2 (that included 93 cases received intralesional TAC alone). All cases were examined before each injection and a final assessment was conducted thirteen weeks following first dose using Vancouver Scar Scale (VSS), Verbal Rating Scale (VRS), pain, itching and patients’ satisfaction. Results: Mean VSS in group 1 was statistically significantly diminished than that in group 2 during the follow-up. Regarding pain and itching, there was insignificant difference between both groups during the follow-up. Regarding patient satisfaction, (96.8%) of group 1, and (92.5%) of group 2 were satisfied. Regarding side effects, ulcer was significantly higher in group 1 while hypopigmentation was significantly increased in group 2. Conclusion: Combination of both TAC and 5FU were demonstrated to be effective in keloid management due to different causes.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".