Modified overlap SMAS flap in the treatment of facial depression after resection of benign parotid lesions
Bibliographic record
Abstract
OBJECTIVE: The purpose of our study was to evaluate the clinical effect of a facelift combined with a modified overlapping superficial musculoaponeurotic system (SMAS) flap for repairing facial depression caused by the resection of benign parotid lesions. METHODS: This retrospective, non-randomized observational study included 87 patients diagnosed with benign parotid tumors who underwent surgical treatment between June 2014 and September 2023. All patients were treated in our department or by the same surgical team after institutional transfer. All patients received surgery via a standardized facelift incision approach.; All patients underwent surgery using a standardized facelift incision. Of them, 58 patients received reconstructive surgery with the modified SMAS flap, and 29 patients were treated using the classical SMAS flap. The degree of satisfaction with scarring and facial depression was assessed 6 months postoperatively using the Vancouver Scar Scale (VSS) and a 10-point scale (0 = no obvious depression; 10 = severe depression), respectively. RESULTS: There was no significant difference regarding the degree of satisfaction with scarring between the two groups (P > 0.05), while the modified SMAS flap group was significantly more satisfied with the facial depressions than the classical SMAS flap group (P < 0.001). Surgical complications and tumor recurrence were not significantly different between the two groups. CONCLUSIONS: A facelift combined with a modified SMAS flap can effectively repair the facial depressions caused by the resection of benign parotid lesions and achieve good aesthetic results.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".