Clinicopathological Risk Factors for Contralateral Lymph Node Metastases in Intraoral Squamous Cell Carcinoma: A Study of 331 Cases
Bibliographic record
Abstract
The current study aimed to examine the effects of clinicopathological factors, including the region, midline involvement, T classification, histological grade, and differentiation of the tumor on the rate of contralateral lymph node metastasis for oral squamous cell carcinoma and to assess their effects on survival rates. A total of 331 patients with intraoral squamous cell carcinomas were included. The influence of tumor location, T status, midline involvement, tumor grading, and the infiltration depth of the tumor on the pattern of metastasis was evaluated. Additionally, the effect of contralateral metastases on the prognosis was examined. Metastases of the contralateral side occurred most frequently in squamous cell carcinomas of the palate and floor of the mouth. Furthermore, tumors with a high T status resulted in significantly higher rates of contralateral metastases. Similarly, the midline involvement, tumor grading, existing ipsilateral metastases, and the infiltration depth of the tumor had a highly significant influence on the development of lymph node metastases on the opposite side. Oral squamous cell carcinomas require a patient-specific decision. There is an ongoing need for further prospective studies to confirm the validity of the prognostic factors described herein.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".