Neonatal Curettage of Large to Giant Congenital Melanocytic Nevi Under Local Anesthetic: A Case Series With Long-Term Follow Up
Bibliographic record
Abstract
BACKGROUND: Neonatal curettage of large to giant congenital melanocytic nevi (L-GCMN) is a simple, minimally invasive procedure typically performed within the first 2 weeks of life. OBJECTIVES: To retrospectively review our experience with serial curettage of L-GCMN in the neonatal period performed under local anesthesia and their long-term outcomes. METHODS: Curettage was performed by a single pediatric dermatologist on nine neonates with L-GCMN under local anesthetic and with oral analgesia between 2002 and 2016 in Red Deer, Alberta, Canada. Patient charts were reviewed retrospectively to assess patient and procedure characteristics, tolerability, safety, cosmetic and functional outcomes, and malignant transformation. RESULTS: Patients were treated with an average of 6 curettage sessions (range 3 to 15) to remove the majority or entirety of the nevus. All patients tolerated local anesthesia well. The most common adverse event of the procedure was transient neutropenia. Two patients developed positive bacterial cultures without clinical signs of infection, treated with antibiotics. All curetted specimens demonstrated benign pathology. Patients were followed annually thereafter, for an average of 6 years. Eight patients with L-GCMN of the trunk had minimal to partial repigmentation with good cosmetic outcome. One patient had recurrence of a facial nevus. None of the patients developed cutaneous malignant melanoma. CONCLUSIONS: Curettage appears to be a safe and effective treatment option for select cases of L-GCMNs of the trunk. We do not recommend the procedure for face or scalp CMN. This procedure can be performed under local anesthesia with serial curettage to avoid potential risks of general anesthesia.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".