Impact of positive frozen section microscopic tumor cut‐through revised to negative on oral carcinoma control and survival rates
Bibliographic record
Abstract
BACKGROUND: The objective of the study was to evaluate the prognostic and therapeutic implications of an initial positive frozen section margin that was revised until negative (microscopic tumor cut-through), and to analyze the influence of microscopic margin status on oral carcinoma control. METHODS: The approach in our investigation was through a retrospective review of patients treated with primary surgery, with frozen section margin control in oral carcinoma. Inclusion criteria included availability of frozen and permanent section histology reports of resection margins and negative final resection margins. RESULTS: Of 547 patients studied, 175 received adjuvant radiation. Local and regional control and disease-specific survival rates were 81.6%, 78.4%, and 76.3%, respectively. Tumor cut-through and pathologic nodal (pN) stage had an independently adverse effect on local control. Tumor cut-through adversely affected cancer control and survival, but this effect diminished significantly in the absence of regional disease. CONCLUSIONS: Microscopic tumor cut-through revised to negative margins is a powerful prognosticator that is observed only when regional disease is also present. The value of adjuvant therapeutic regimens is questionable in patients with microscopic tumor cut-through, revised to negative margins, and with no regional disease.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".