Tumor Bed Margins Versus Specimen Margins in Oral Cavity Cancer: Too Close to Call?
Bibliographic record
Abstract
Introduction The routine assessment of intraoperative margins has long been the standard of care for oral cavity cancers. However, there is a controversy surrounding the best method for sampling surgical margins. The aim of our study is to determine the precision of a new technique for sampling tumor bed margins (TBMs), to evaluate the impact on survival and the rate of free flap reconstructions. Methods This retrospective cohort study involved 156 patients with primary cancer of the tongue or floor of the mouth who underwent surgery as initial curative treatment. Patients were separated into 2 groups: one using an oriented TBM derived from Mohs’ technique, where the margins are taken from the tumor bed and identified with Vicryl sutures on both the specimen and the tumor bed, and the other using a specimen margins (SMs) driven technique, where the margins are taken from the specimen after the initial resection. Clinicopathologic features, including margin status, were compared for both groups and correlated with locoregional control. Precision of per-operative TBM sampling method was obtained. Results A total of 156 patients were included in the study, of which 80 were in TBM group and 76 were in SM group. Precision analysis showed that the oriented TBM technique pertained a 50% sensitivity, 96.6% specificity, 80% positive predictive value, and an 87.5% negative predictive value. Survival analysis revealed nonstatistically significant differences in both local control (86.88% vs 83.50%; P = .81) as well as local-regional control (82.57% vs 72.32%; P = .21). There was a significant difference in the rate of free flap-surgeries between the 2 groups (30% vs 64.5%; P < .001). Conclusion Our described oriented TBM technique has demonstrated reduced risk of free flap reconstructive surgery, increased precision, and similar prognostic in terms of local control, locoregional control, and disease-free survival when compared to the SM method.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".