The efficacy of intralesional triamcinolone acetonide (20mg/ml) in the treatment of keloid
Bibliographic record
Abstract
Background: Management of keloid is difficult as well as challenging. Intralesional triamcinolone acetonide (TAC) injections have remained a gold standard in non-surgical management of keloid. TAC is generally used in the concentration of 40mg/ml, which causes adverse effects such as local dermal atrophy and hypopigmentation. Aim was to study efficacy and adverse effects of TAC in treatment of keloid, in a lesser concentration of 20mg/ml.Methods: An open label study was conducted from November 2015 to May 2017 on 25 subjects of either gender, in the age group 11-55 years, at a medical college hospital. Intralesional injection TAC 20 was administered in the keloid at an interval of 3 weeks, for a total of 6 sessions, over a period of 18 weeks. Vancouver scar scale (VSS) was used to assess the improvement and SPSS 21 for statistical analysis.Results: Mean age of keloid subjects was 30.72 years and median duration of keloid was 8 months. The mean VSS score before treatment was 8.36 which reduced to 3.20 after treatment. Mean percentage change in VSS score was 62.79%, which was very highly significant (p <0.001). Physician’s assessment was ‘Very Good’ in 52.0% and ‘Excellent’ in 5 (20%). Adverse effect of atrophy was seen in 3 (12%), hypopigmentation in 11 (44%) and telangiectasia in 4 (16%).Conclusions: Intralesional injection triamcinolone acetonide 20mg/ml gives very good to excellent improvement in the majority of patients of keloid. Local adverse effects seen were hypopigmentation, atrophy and telengiectasia.
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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) | 0.000 | 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) | 0.001 | 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".