The clinical application of ear keloid using five-blade core excision combined with pressure and superficial electron beam radiation
Bibliographic record
Abstract
Background Surgical removal is the primary method for the clinical treatment of ear keloids. However, there are numerous surgical options available, and no standardized approach in the literature. Objectives This study aimed to evaluate the impact of five-blade core excision on the removal of ear keloids. Methods A preliminary study involving 11 patients (21 lesions) with ear keloids was conducted between January 2023 and December 2023. Five-blade core excision was performed; superficial electron beam radiotherapy was administered at a dose of 4 Gy for 5 post-operative consecutive days, and pressure clips were applied for 6 months. The Vancouver Scar Scale (VSS) and the Patient and Observer Scar Assessments Scale (POSAS) were used to assess the results. Results The mean age of the patients was 24.36 years (18–44 years). Postoperative follow-up ranged from 20 months. The patients underwent 5 days of postoperative radiotherapy and pressure clips for 6 months. Nine patients had no recurrence, whereas two patients had a mild recurrence (one patient rejected radiotherapy). The VSS and POSAS scores significantly decreased ( p < 0.01). Conclusion Five-blade core excision combined with pressure and superficial electron beam radiotherapy demonstrates effective therapeutic outcomes for ear keloid.
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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) | 0.000 | 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".