Good and bad indications for adjuvant radiotherapy after transoral laser microsurgery for laryngeal cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize current evidence regarding the indication of adjuvant treatment after transoral laser microsurgery (TOLMS). RECENT FINDINGS: Apart from well known risk factors, margins represent the key point in the decision-making. If margins are affected, additional treatment is mandatory. One exception could be the presence of one superficial margin in early tumors that can be strictly followed up by fiberendoscopy. As a general rule, the best option is margin-revision surgery by repeating TOLMS or switching to open partial surgery. (Chemo)radiotherapy can be also considered, being total laryngectomy the last alternative. In locally advanced tumors with uncertain margins (e.g. posterior paraglottic space invasion, vertical anterior commissure reaching the cartilage during primary resection), adjuvant treatment may improve local control with laser but with little impact on disease-specific or overall survival. In this scenario, QoL may be in part reduced after radiotherapy, although recent studies suggest that functional outcomes are favorable. Therefore, decision should be discussed individually with the patient, especially if a total laryngectomy is the only alternative after a possible relapse. SUMMARY: Considerable work needs to be done to identify those cases that may benefit from adjuvant treatment after TOLMS, including a detailed description of functional outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".