Aesthetic Submandibular Gland Resection: A Review of Complication Incidence and Prevention
Bibliographic record
Abstract
Achieving optimal neck contour during facial rejuvenation may require addressing the submandibular glands, as supraplatysmal fat removal alone is insufficient for many patients, particularly those with fuller necks. The purpose of this study is to review the rate of complications associated with removal and techniques to improve safety. A comprehensive search was conducted to identify literature on complications associated with submandibular gland removal in the context of facial rejuvenation. Studies were included if they provided patient data and complication rates following surgical outcomes. Screening of 908 articles identified 11 (1.21%) studies on complication rates related to submandibular gland resection for aesthetic purposes. A total of 3379 participants were included, of which 48.86% (n = 1651) underwent submandibular gland resection, with a mean age of 50.4 years (range, 32-83), and 88.53% (n = 247) were female. Complications included hematomas (1.15%, n = 16), requiring reoperation in 23.08% (n = 3) of cases, sialoceles (1.33%, n = 21), salivary fistulas (0.82%, n = 6), nerve injury (3.97%, n = 40), xerostomia (0.13%, n = 1), and neck induration (21.43%, n = 21), with complication rates varying across studies. Submandibular gland reduction offers aesthetic benefits but comes with potential risks. Effective preoperative planning, meticulous gland mobilization, and proper exposure are essential for minimizing risks. Advances in techniques, such as improved dissection methods, the use of botulinum toxin, netting techniques, and energy-based instruments like LigaSure (Medtronic, Minneapolis, MN), have enhanced the safety of the procedure. While complications can arise, they typically resolve within a few months, and the overall outcomes improve with surgical experience. The associated risks and benefit profile should be discussed thoroughly with patients. Level of Evidence: 3 (Therapeutic).
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".