Liposuction for Submental Lymphedema Improves Appearance and Self‐Perception in the Head and Neck Cancer Patient
Bibliographic record
Abstract
OBJECTIVE: Patients who have undergone treatment for head and neck cancer are at risk for neck lymphedema, which can severely affect quality of life. Liposuction has been used successfully for cancer patients who suffer from posttreatment limb lymphedema. The purpose of our study was to review the outcomes of head and neck cancer patients at our center who have undergone submental liposuction for posttreatment lymphedema. STUDY DESIGN: Prospective cohort study. SETTING: Oncology center in tertiary hospital setting. SUBJECTS AND METHODS: Head and neck cancer patients who underwent submental liposuction for posttreatment lymphedema were included. Nine patients met the study criteria. Patients completed 2 surveys (Modified Blepharoplasty Outcome Evaluation and the validated Derriford Appearance Scale) pre- and postoperatively to assess satisfaction. Patients' pre- and postoperative photos were graded by independent observers to assess outcomes objectively. RESULTS: Our study demonstrated a statistically significant improvement in patients' self-perception of appearance and statistically significant objective scoring of appearance following submental liposuction. CONCLUSION: Submental liposuction improves the appearance and quality of life for head and neck cancer patients suffering from posttreatment lymphedema by way of improving their self-perception and self-confidence.
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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) | 0.002 | 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".