Predictive value of tumor thickness for cervical lymph‐node involvement in squamous cell carcinoma of the oral cavity
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Bibliographic record
Abstract
BACKGROUND: Tumor thickness (TT) appears to be a strong predictor for cervical lymph-node involvement in squamous cell carcinoma of the oral cavity (OSCC), but a precise clinically optimal TT cutoff point has not been established. To address this question, the authors conducted a meta-analysis. METHODS: All relevant articles were identified from MEDLINE and EMBASE as well as from cross-referenced publications cited in relevant articles. Lymph-node involvement was confirmed and identified as positive lymph-node declaration (P(LN)D) by either pathologic positivity on immediate neck dissection or by neck recurrence identified after follow-up > or = 2 years. Odds ratios (OR) were calculated to quantify the predictive value of TT. Negative predictive values (and the percentage of patients falsely predicted to not have P(LN)D [FN-P(LN)D]) were compared to determine the optimal TT cutoff point. RESULTS: Sixteen studies were selected from 72 potential studies, yielding a pooled total of 1136 patients. Data were examined for the following TT cutoff points: 3 mm (4 studies, 387 patients), 4 mm (9 studies, 778 patients), 5 mm (6 studies, 367 patients), and 6 mm (4 studies, 488 patients). The OR (95% CI) was 7.3 (5.3-10.1) for the overall group. The proportion of FN-P(LN)D was 5.3% (95% CI, 2.0-11.2), 4.5% (2.6-7.2), 16.6% (11.5-22.8), and 13.0% (9.7-16.9) for TT<3, <4, <5, and <6 mm, respectively. There was a statistically significant difference between the 4-mm and 5-mm TT cutoff points (P = .007). CONCLUSIONS: TT was a strong predictor for cervical lymph-node involvement. The optimal TT cutoff point was 4 mm.
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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) | 0.002 | 0.001 |
| 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 it