A Simple Approach for the Repair of Intermediate-to-Large Cheek Defects
Bibliographic record
Abstract
BACKGROUND: Surgical management of cheek congenital melanocytic nevus (CMN) remains a huge challenge because of undesirable defects in repair. The use of direct closure is often limited to defect reconstruction with a diameter less than 4 cm. OBJECTIVE: This study aimed to evaluate the safety and efficacy of direct vertical closure combined with extensive subcutaneous tissue undermining boundaries for intermediate-to-large cheek defects. METHODS AND MATERIALS: A retrospective review was conducted to evaluate patients with cheek CMN who underwent the aforementioned procedure. Projected adult size, defect size, and incision length were measured. The Vancouver scar scale and visual analog scale were applied to assess scar formation and postoperative appearance. Complications within 1 year postoperatively were recorded. RESULTS: A total of 35 patients with CMN >3.5 cm underwent the procedure. Patients' age ranged from 3 to 36 years. The average projected adult size of the facial CMN was 5.5 ± 1.6 cm. The mean Vancouver scar scale and visual analog scale scores were 2.6 ± 1.0 and 8.0 ± 0.7, respectively. There were 2 cases of dog ear deformity (5.7%) and 1 case of hematoma (2.9%). CONCLUSION: This simple algorithm yields satisfying results with low complication rate in the repair of intermediate-to-large cheek defects and may become a useful alternative to cheek reconstruction.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".