Submental liposuction for the management of lymphedema following head and neck cancer treatment: A randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: 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 in cancer patients who suffer from post-treatment 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 post-treatment lymphedema and compare their subsequent results with a control group. METHODS: All head and neck cancer patients at an oncology center in tertiary hospital setting who complained to their attending surgeon or radiation oncologist regarding cervical lymphedema secondary to head and neck cancer treatment, and had been disease-free for a minimum of one year, with no previous facial plastic surgical procedures were eligible for inclusion into the study. Study design was a non-blinded randomized controlled trial. Twenty patients were randomized into a treatment arm (underwent submental liposuction n = 10) and control arm (n = 10). Both groups of patients completed two surveys (Modified Blepharoplasty Outcome Evaluation and the validated Derriford Appearance Scale) on initial office visit after consenting for the trial. The treatment group then completed the surveys 6 months post-operatively while the control group filled the surveys 6 months after the initial assessment but had no intervention. Mann-Whitney U tests were performed to compare the responses of those that did and did not receive liposuction. RESULTS: Our study demonstrated a statistically significant improvement in patients' self-perception of appearance and statistically significant subjective scoring of appearance following submental liposuction. CONCLUSIONS: Submental liposuction is an effective and safe procedure to improves the quality of life for head and neck cancer patients suffering from post-treatment lymphedema.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".