Combination of keloid core excision and triamcinolone acetonide local injection shows significant clinical efficacy in treating auricular keloid
Bibliographic record
Abstract
OBJECTIVES: To investigate the clinical efficacy of keloid core excision combined with triamcinolone acetonide (TA) local injection for the treatment of auricular keloids. METHODS: From May 2019 to November 2021, 86 patients with auricular keloid who met the inclusion criteria were enrolled. Based on treatment modality, they were divided into two groups: a research group (n=43) receiving keloid core excision combined with TA local injection and a control group (n=43) undergoing keloid core excision alone. The clinical efficacy, postoperative adverse reactions, Vancouver Scar Scale (VSS) score 12-Item Pruritus Severity Scale (12-PSS) score, sleep quality, serological indicators, Self-Rating Anxiety Scale (SAS) score, Self-Rating Depression Scale (SDS) score, Short-Form 36-Item Health Survey (SF-36) scores, recurrence rate, and treatment satisfaction were compared between the two groups. RESULTS: The research group showed superior overall clinical efficacy, higher SF-36 scores, fewer total adverse reactions, and better sleep quality compared to the control group. Additionally, the research group had lower VSS, 12-PSS, SAS, and SDS scores, more significant reductions in serological indicators, and a reduced recurrence rate. CONCLUSIONS: Keloid core excision combined with TA local injection was more effective than keloid core excision alone in treating auricular keloids, with significantly better clinical efficacy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".