Improving aesthetic outcomes after head and neck reconstruction
Bibliographic record
Abstract
PURPOSE OF REVIEW: Tremendous advancements have been made in head and neck reconstruction following oncologic resection. Despite this, many patients are left with disfiguring postoperative changes. The focus of this review highlights various techniques aimed at improving aesthetic outcomes following head and neck cancer therapy, with a focus on liposuction and fat augmentation. RECENT FINDINGS: Over the past decade, the use of liposuction in treating lymphedema after head and neck cancer therapy has showed promising results. Owing to great improvements in harvesting and purification techniques, fat augmentation has been effectively utilized in correcting a wide array of defects. Although free tissue transfer is frequently used in head and neck reconstruction, there is a scarcity of literature on the indications for flap revision procedures. SUMMARY: Head and neck reconstructive surgery can lead to significant cosmetic and functional morbidity. Several tools are available to help improve aesthetic outcomes in this patient population. A thorough understanding of the various techniques and their indications is essential for achieving optimal results.Video abstract available: See the Video Supplementary Digital Content 1, http://links.lww.com/COOH/A34.
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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 | 0.001 | 0.002 |
| 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) | 0.006 | 0.001 |
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".