Eradication of keloids: Surgical excision followed by a single injection of intralesional 5-fluorouracil and botulinum toxin
Bibliographic record
Abstract
BACKGROUND: Keloids may complicate wound healing secondary to trauma, inflammation or surgical incision. Although various treatment modalities have been used with variable degrees of success, overall recurrence rates have remained unacceptably high. METHODS: The present study involved 80 patients with keloids of at least one-years' duration. Following total surgical excision of the keloid, a single dose of 5-fluorouracil was injected into the edges of the healing wound on postoperative day 9 together with botulinum toxin. The concentration of 5-fluorouracil used was 50 mg/mL and approximately 0.4 mL was infiltrated per cm of wound tissue, with the total dose <500 mg. The concentration of botulinum toxin was 50 IU/mL with the total dose <140 IU. RESULTS: Patients were followed-up for 17 to 24 months and a recurrence rate of 3.75% was found, which was significantly lower than in previously reported studies using other therapeutic modalities. CONCLUSION: The author recommends that this treatment be routinely applied to all keloids because it is significantly more effective than those described by other authors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".