The buccinator flap
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article reviews the buccinator flap, its relevant surgical anatomy, surgical technique, and applications in head and neck reconstruction. RECENT FINDINGS: Reconstruction of defects after extirpation of head and neck neoplasms can be challenging and complex. Various reconstructive techniques, including skin grafts, regional flaps, and free tissue transfer, can be employed in restoration of form and function after ablative surgery. Although a wide array of reconstructive options is available, the technique employed will largely depend on the surgeon preference and experience. Commonly used pedicled flaps and free tissue transfer techniques can share several disadvantages, including prolonged operative time, poor cosmesis, donor site morbidity, functional limitations, and excessive tissue bulk for intraoral reconstruction. The ideal reconstructive method would adhere to the plastic surgery principle of replacement of 'like with like'. The ease of dissection and useful application makes the buccinator flap an excellent additional reconstructive option for the head and neck surgeon. SUMMARY: The buccinator myomucosal flap is a versatile and dependable flap for head and neck reconstruction. Its minimal donor site morbidity, pliability, ease of elevation, and flexible usage while offering optimal functional and cosmetic results make it a viable option for select defects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".