Effect of Epidermoid Cysts on the Efficacy of Intralesional Corticosteroid Therapy for Hypertrophic Scars and Keloids: A Prospective Pilot Study
Bibliographic record
Abstract
BACKGROUND: Patients with hypertrophic scars (HSs) or keloids occasionally have epidermoid cysts (ECs), and the effect of ECs on the effectiveness of intralesional corticosteroids (ILCs) treatment in these patients has not been reported. OBJECTIVE: This study aims to evaluate the influence of ECs on the outcomes of ILCs treatment in patients with HSs or keloids. MATERIALS AND METHODS: This prospective study included 572 patients with keloids ( n = 461) or HSs ( n = 111). Patients received intralesional triamcinolone acetonide injection (0.05 mL/injection) at a concentration of 40 mg/mL and every 28 days for 4 sessions, with a 1-year follow-up. RESULTS: A higher incidence of ECs was observed in keloid patients (16.92%) compared with HSs patients (7.21%). Keloid patients with ECs were older ( p = .008) and had a longer disease duration ( p = .0148), higher Vancouver scar scale (VSS) scores ( p = .04), and greater thickness ( p = .006). Keloid patients with ECs showed less improvement in VSS scores ( p < .0001) and thickness ( p < .0001) after ILCs treatment, with a higher recurrence rate ( p < .0001). The overall complication rate in keloid patients with ECs after ILCs treatment was 49.51%. CONCLUSION: Epidermoid cysts under keloids were associated with a poor response to ILCs therapy. Therefore, it is recommended to incorporate ultrasonography as a routine examination for keloid patients to aid in better decision making in clinical practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".