Intralesional steroid injection in keloid ear — a prospective observational study
Bibliographic record
Abstract
Abstract Background Keloid is a fibro-proliferative dermal benign growth affecting the Asian population. In India, ear keloids are common, often resulting from ear piercing, a prevalent cultural practice. The resultant ear keloids pose aesthetic concerns, leading to significant psychological distress, and necessitating effective treatment. While various treatment options are available, their outcomes and recurrence rates vary, highlighting the need for individualized and optimal management strategies. Our study aimed to observe the combined effect of intralesional triamcinolone with hyaluronidase on keloid regression. Methods This prospective observational study was conducted at our tertiary care institute over 1 and a half years. Fifty patients who met the inclusion criteria were enrolled and received intralesional triamcinolone injection with hyaluronidase. Keloid regression was assessed using the Vancouver Scar Scale (VSS), while the visual analogue score (VAS) and patient satisfaction score (PSS) provided subjective evaluations of symptom relief. All patients underwent compression therapy. Patients were evaluated at every visit and then at 1 year for symptom relief, keloid regression, and complications if any. Results Demographic data of all patients were recorded. Ear keloids were predominantly observed in females, with a male-to-female ratio of 18:32. The most common etiology was trauma following ear piercing, accounting for 46% of cases. All patients showed improvement in VSS, VAS, and PSS scores in the follow-up visits. A total of 98% of patients demonstrated a complete treatment response, with only a single instance of recurrence. Conclusion Intralesional triamcinolone with hyaluronidase provides satisfactory symptomatic relief and has lower recurrence rates. All patients showed improvement in the Vancouver Scar Scale and had enhanced VAS and PSS scores.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".