Bibliographic record
Abstract
Sixty-six children with large congenital nevi of the face were surgically managed in our center during the last 8 years. All patients with a lesion that posed a reconstructive challenge were included in the study. None could be effectively dealt with by excision and simple primary closure. To simplify description and evaluation, the patients were divided into three groups. Group I had 15 patients with relatively small lesions (1- to 3-cm maximal diameter) that were confined to one aesthetic unit of the face and could be reconstructed in one stage. Reconstruction was usually achieved by using local skin flaps or with full-thickness skin grafting. Group II had 28 patients with medium-sized lesions (3- to 12-cm maximal diameter) that involved one or two aesthetic units and required not more than two stages for reconstruction. These patients usually needed either serial excisions and/or skin grafting or a two-stage tissue expansion procedure (insertion of tissue expanders and reconstruction). Group III had 23 patients with very large lesions (over 12 cm in maximal diameter), some covering half of the face and thus involving several aesthetic units and requiring multiple stages for reconstruction. These patients required a combination of tissue expansion procedures, large faciocervical and scalp/forehead skin flaps, full-thickness skin grafting, and serial excisions for reconstruction. The anatomic distribution of the lesions over the various aesthetic units is described, as are the reconstructive techniques with advantages and disadvantages of each, reflecting on outcome. The approach to the larger complex lesions is detailed. Based on our experience, a reconstructive algorithm is proposed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".