Predictive value of tumor thickness for cervical lymph‐node involvement in squamous cell carcinoma of the oral cavity
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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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.017 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".