Complete frozen section margins (with measurable 1 or 5 mm thick free margin) for cancer of the tongue: part 2: clinical experience.
Bibliographic record
Abstract
OBJECTIVE: To obtain completely negative margins of 1 to 5 mm at the time of surgery for oral tongue squamous cell carcinoma by using a Mohs-like technique. STUDY DESIGN: Case series of 12 patients (4 T1, 5 T2, 2 T3, 1 T4) and a review of the literature. RESULTS: For the first six cases, complete, colored for precise orientation, frozen margins of high quality were obtained in a relatively short time (20-75 minutes). Four levels were evaluated within 1 to 2 mm of the line of resection. Obtaining complete free margins for a thickness of 5 mm was done for the last six cases. The time was longer (70-120 minutes) but did not exceed the time necessary to perform the neck dissection, except for one patient. The technique using the scalpel and scissors implied slightly more bleeding, which was never a problem. We have observed no recurrence for these 12 patients (follow-up 12-34 months). CONCLUSION: The review of the literature demonstrates that invaded and close margins confer a higher recurrence rate. We have obtained 1 to 2 mm (first six patients) and 5 mm (last six patients) thick, complete, oriented, and free frozen margins with success and no recurrence, but the follow-up was short. We prefer to obtain a 5 mm thick margin when possible. The delay to obtain the pathologic result is reasonable. This approach should reduce dramatically the problem of positive and close margins at the final pathology and, consequently, the rate of local control.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".