The submental island flap in head and neck reconstruction
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article reviews the submental island flap, focusing on its relevant surgical anatomy, surgical technique, and recent applications in head and neck reconstruction. RECENT FINDINGS: Reconstruction of defects after extirpation of head and neck neoplasms is a highly challenging and complex surgical undertaking. Various techniques, including skin grafts, regional flaps, and free tissue transfer, can be employed in the restoration of function and cosmesis after ablative surgery. However, commonly used techniques, such as the radial forearm flap or anterior lateral thigh flap, share numerous disadvantages, such as donor-site morbidity, poor color match for cutaneous reconstruction, and excessive tissue bulk for intraoral reconstruction, making ideal replacement of 'like with like' elusive. The use of a submental artery island flap is a relatively new and an increasingly popular option for head and neck reconstruction that provides an additional reconstructive option. SUMMARY: The submental island flap is a reliable and versatile flap for head and neck reconstruction. Its minimal donor site morbidity, excellent cosmetic match, pliability, and relative ease of dissection and application has a definite advantage over distant flaps, making it an excellent addition to the reconstructive armamentarium of the head and neck surgeon.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".