What Do We Know About Treating Recalcitrant Auricular Keloids? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Recalcitrant auricular keloids are keloids that have recurred after any previous treatment. They have been shown to have an increased likelihood of recurrence. There is no consensus on how best to treat recalcitrant auricular keloids. Here, we perform the first systematic review and meta-analysis investigating the evidence for treating recalcitrant auricular keloids. METHODS: We searched MEDLINE, EMBASE, CINAHL, and EBM Reviews using specific keywords. Prespecified inclusion and exclusion criteria were used to assess article eligibility. Data were extracted for number of recalcitrant keloids, treatment modality, recurrence, and minimum follow-up time. Included articles were stratified by treatment and assigned a level of evidence (LOE) based on the Oxford Centre for Evidence-Based Medicine guidelines. A meta-analysis was performed to estimate recurrence rates with 95% confidence intervals for each treatment modality. RESULTS: A total of 887 unique articles were identified and 13 included. Eleven were LOE III and 2 were LOE IV. Recurrence rates were found to be 9% (95% CI: 3%-25%) for excision with adjuvant brachytherapy, 14% (95% CI: 12%-17%) for excision with adjuvant compression therapy, 17% (95% CI: 3%-56%) for excision with adjuvant external beam radiation, and 18% (95% CI: 4%-53%) for excision with adjuvant steroid injections. No statistical significant difference was found. CONCLUSIONS: Data for treatment of auricular keloids are heterogeneous with few high-quality studies. Excision with adjuvant brachytherapy has the lowest recurrence rate in our analysis. Narrow confidence intervals reported here for brachytherapy and compression therapy may help surgeons more confidently recommend either of these treatment modalities to patients.
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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 | 0.021 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.043 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".