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 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".