Combined Surgical Excision and Radiation Therapy for Keloid Treatment
Bibliographic record
Abstract
Various methods have been attempted for the treatment and management of keloids; however, there is little satisfactory clinical evidence in long-term follow ups. Also, there is a preference for occurrence and recurrence in anatomic location. Usually anatomic locations with higher regional tension and more sebaceous glands are inclined toward pathogenesis. Thirty-eight keloids treated with combined surgical excision and postoperative irradiation, using electron beams with only a 10-mm opening by lead shielding, were investigated at a mean follow up of 4.4 +/- 2.5 years (range, 1-9 years) at a single institute. Ten locations such as the ear (n = 6), neck (n = 3), and upper lip (n = 1) were among the craniofacial locations. The hardness of the keloids and posttreatment scars was clinically and objectively tested with the Vancouver scar scale and a durometer, which is often used for the industrial measurement of thread balls and rubber. At a mean of 4.4 +/- 2.5 years of follow up, the clinical characteristics of the scars were significantly better posttreatment as 2.6 +/- 0.5 versus 1.0 +/- 0.6, 3.7 +/- 0.7 versus 1.7 +/- 0.7, 2.9 +/- 0.4 versus 1.3 +/- 0.5, and 2.7 +/- 0.5 versus 1.3 +/- 0.5 (keloid scars versus posttreatment scars: pigmentation, pliability, height and vascularity, respectively, P < 0.01). The durometer readings were significantly lower posttreatment, 15.2 +/- 3.9 versus 7.7 +/- 2.9 (keloid scars versus posttreatment scars, P < 0.01). The recurrence rate was 21.2% overall with none in craniofacial locations. Therefore, the combined treatment of surgical excision and postoperative electron beam irradiation is effective for scar quality and reducing the recurrence rate in long-term follow up.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".