Selective Neck Dissection in Node‐Positive Squamous Cell Carcinoma of the Head and Neck
Bibliographic record
Abstract
OBJECTIVE: The optimal type of neck dissection in head and neck squamous cell carcinoma (SCC) with clinical cervical metastases has not been determined. The following study was performed to determine the rate of regional control with selective neck dissection (SND) in these patients. STUDY DESIGN: Case series with planned data collection. SETTING: Single institution, cancer center. METHODS AND SUBJECTS: Patients with cervical lymph node metastases from mucosal cancers of the head and neck who were treated with SND from 2000 to 2010 were selected. Demographics, tumor characteristics, extent of neck dissection, adjuvant treatments, locoregional control, and survival were recorded. Recurrence in the neck and disease-specific survival (DSS) were primary and secondary end points. RESULTS: One hundred eight patients underwent SND. Sixty-nine (64%) were male. Median age was 62 (20-89) years. The most common primary site was the oral cavity (71.3%). Ninety-five (88%) received adjuvant treatment. Median follow-up was 21 months. Six patients (5.5%) had isolated recurrence in the dissected neck. Patients with N2C disease had poorer neck recurrence-free survival. At the end of study, 64 (59.3%) patients had no evidence of disease, and 23 (21.3%) had died of disease. Two-year DSS was 76.9%. Number of positive nodes (P = .026) and positive surgical margins (P = .001), among others, were predictors of poorer DSS. CONCLUSION: In a highly selected group of patients with cervical lymph node metastases from head and neck SCC, selective neck dissection is effective in controlling the disease in the neck when performed in the setting of a multimodality treatment, including adjuvant radiotherapy or radiochemotherapy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".