Lymph node yield: Impact on oncologic outcomes in oral cavity cancer
Bibliographic record
Abstract
BACKGROUND: Lymph node metastases are associated with poor prognosis in oral cavity squamous cell carcinoma (OCSCC). In other cancers, clinical guidelines on the number of lymph nodes removed during primary surgery, lymph node yield (LNY), exist. Here, we evaluated the prognostic capacity of LNY on regional failure, locoregional recurrence, and disease-free survival (DFS) in patients with OCSCC treated by primary neck surgery. METHODS: This retrospective cohort study took place at Sunnybrook Health Sciences Centre in Toronto, Canada and involved a chart review of all adult patients with treatment-naive OCSCC undergoing primary neck dissection. For each outcome, we first used the maximally selected rank statistics and an optimism-corrected concordance to identify an optimal threshold of LNY. We then used a multivariable Cox proportional hazards model to assess the association between high LNY (>threshold) and each outcome. RESULTS: Among the 579 patients with OCSCC receiving primary neck dissection, 61.7% (n = 357) were male with a mean age of 62.9 years (standard deviation: 13.1) at cancer diagnosis. When adjusting for sociodemographic and clinical factors, LNY >15 was significantly associated with improved DFS (adjusted HR [aHR]: 0.73, 95% CI: 0.54-0.98), locoregional recurrence (aHR: 0.68, 95% CI: 0.49-0.95), and regional failure (aHR: 0.61, 95% CI: 0.39-0.93). CONCLUSIONS: Our study findings suggested high LNY to be a strong independent predictor of various patient-level quality of surgical care metrics. The optimal LNY we found (15) was lower than the conventionally recommended (18), which calls for further research to establish validity in practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".