Diagnostic utility of central node necrosis in predicting extracapsular spread among oral cavity squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Oral cavity squamous cell carcinoma (SCC) represents the most common SCC affecting the head and neck region. Long-term survival of patients with oral cavity SCC is adversely affected by lymph node metastasis and further decreased by the presence of lymph node extracapsular spread (ECS). METHODS: Using a case-control design, preoperative CT scans from patients with oral cavity SCC and metastatic lymphadenopathy were evaluated by 2 independent neuroradiologists, blinded to the study, for a number of radiologic parameters, including central node necrosis. Multivariate logistic regression was used to identify parameters independently predicting pathologic ECS. RESULTS: For both neuroradiologists, central node necrosis was a significant predictor of ECS, with high interrater agreement (kappa = 0.71). On multivariate analysis, only central node necrosis independently predicted ECS (odds ratio [OR] = 12.1; 95% confidence interval [CI] = 1.24-119). Central node necrosis predicted ECS with 91% sensitivity and 88% negative predictive values. CONCLUSION: Our findings suggest that central node necrosis on preoperative CT scans is strongly associated with the presence of ECS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".