Intralesional cryotherapy with triamcinolone and onabotulinumtoxinA injections for umbilical keloid: A case report
Bibliographic record
Abstract
Introduction Keloid scars are therapeutically challenging and although many treatment options exist, there are no specific guidelines, and few reports have discussed keloids in the umbilical region. Methods Here, we present a successful treatment of a 31-year-old female with a history of a recurrent keloid in the umbilical region. The keloid was treated using intralesional cryotherapy followed by intralesional onabotulinumtoxinA and triamcinolone acetonide injections. Discussion The patient expressed high satisfaction, minimal side effects, and no recurrence. Conclusion Overall, due to the low rate of side effects, high patient satisfaction, and absence of recurrence, this treatment modality should be considered as an option for umbilical keloids. Lay Summary Background to subject: Keloids are a type of scar that are difficult to treat. There are many treatment options available, but there is no single best treatment for keloids that form around the belly button region. Question being asked: Is intralesional cryotherapy with intralesional onabotulinumtoxinA and triamcinolone acetonide injections effective at treating keloids in the belly button region? How the work was conducted: We treated a 31-year-old female with a keloid around the belly button region that returned after prior treatment. The keloid was treated using combination therapy of freezing the keloid from the inside out, which is called intralesional cryotherapy. This was followed by two types of injections, called onabotulinumtoxinA and triamcinolone acetonide, directly into the keloid. What we learned: Overall, due to the low rate of side effects, high patient satisfaction and the keloid not returning, this treatment plan should be considered as an option for keloids in the belly button region. What we did not learn: This treatment may or may not be effective and safe for all patients of all skin types and demographics as this treatment was performed for only one patient.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".