Treatment of Congenital Facial Nevi
Bibliographic record
Abstract
The treatment of congenital facial nevi is often difficult and challenging. Previous authors have reported their techniques, results, and complications when treating these lesions. Our objectives are to simplify the treatment planning by subdividing the lesions with a new classification and using this to formulate a surgical algorithm. One hundred and two patients with congenital facial nevi were reviewed. All of these patients have had surgical excision for the lesions. We have subgrouped the lesions into three groups, according to size, number of aesthetic units involved, and number of reconstructive stages required. Group I included lesions 1 to 3 cm in maximal diameter, within one aesthetic unit, and requiring one or two reconstructive stages. This group included 29 patients. Group II included lesions 3 to 12 cm in maximal diameter, covering one or two aesthetic units, and requiring not more than two stages of reconstruction. This group had 41 patients. Group III consisted of extensive lesions, over 12 cm in maximal diameter, covering several aesthetic units, and requiring several stages of reconstruction. In this group, we had 32 patients. On the basis of our experience in treating congenital facial nevi in this series, we have developed a surgical algorithm for reconstruction. We are optimistic that this will assist the surgeon in surgical planning and treating this complex patient population. The algorithm is arranged according to the new classification of congenital facial nevi that is presented.
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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".