Primary surgery versus (chemo)radiotherapy in oropharyngeal cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Radiotherapy is the traditional treatment for oropharyngeal cancer (OPC) because of its ability to preserve anatomic form and function compared with other conventional curative options. Recently, transoral robotic surgery (TORS) and transoral laser microsurgery (TLM) have emerged prominently for T1-T2 OPC. This review summarizes the recent literature pertaining to OPC outcomes following primary TORS/TLM versus primary radiotherapy with or without chemotherapy and addresses controversies surrounding indications for adjuvant treatment following TORS/TLM. RECENT FINDINGS: Articles regarding OPC outcomes after primary TORS/TLM or radiotherapy/chemoradiotherapy published over the past 12 months were identified. TORS/TLM studies reported encouraging oncologic and functional outcomes. Primary radiotherapy alone showed exemplary results for a similar group of patients. However, comparisons of outcomes between these two primary modalities rely on historical data vulnerable to selection bias, even in a matched cohort study. The majority of cases treated with TORS/TLM also received adjuvant treatment. Soft tissue necrosis complicating this approach has also been reported. Controversies exist regarding the definition of resection margin status, prognostic value of extracapsular spread in human papillomavirus-related OPC and indications for adjuvant treatment following TORS/TLM. SUMMARY: TORS/TLM is an attractive approach for selected T1-T2 OPC, but its role should be refined based on a high level of evidence.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".