Carbon Dioxide Laser Microsurgery for Tongue Cancer: Surgical Techniques and Long-Term Results
Bibliographic record
Abstract
Thirty-seven consecutive patients with cancer of the anterior two-thirds of the tongue without clinical neck lymph nodes or distant metastasis were treated with transoral carbon dioxide (CO2) laser microsurgery. During the operation, a monopolar coagulation-suction device was applied to achieve a clear surgical field. Under a surgical microscope, we repeatedly palpated the soft tissue to identify the tumour margins, in particular the deep margin, to maintain adequate safe margins in three dimensions. We resected the tumour by en bloc procedures rather than by vaporization or debulking. Of the 28 patients in the T1 and T2 groups, 26 patients did not receive postoperative radiotherapy. The local control rate calculated by the methods of Kaplan and Meier in all 37 patients at 5 years was 93.6%. No local recurrence occurred in the T1 or T2 cases. Nine patients suffered from neck recurrence and the neck control rate at 5 years was 74.6%. Eight of these nine patients were salvaged by surgery with adjuvant radiotherapy, and six of them finally achieved disease-free status. The 5-year disease-free survival rate for our series was 88%. Our surgical techniques using CO2 laser microsurgery are effective and advantageous methods for excision of oral tongue cancer, especially stage I and II lesions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".