“Close Then Clear” Margins in Transoral Resection of p16+ Oropharyngeal Squamous Cell Carcinoma
Bibliographic record
Abstract
OBJECTIVE: Standard 5-mm surgical margins in the oropharynx are challenging due to the intimate relationship with neurovascular structures and patient function. De-escalation of therapy with the acceptance of close surgical margins may improve functional patient outcomes. This study assesses locoregional recurrence with initial close (≤2 mm) margins following transoral surgery for p16+ oropharyngeal squamous cell carcinoma (SCC) with or without re-resection and adjuvant therapy. STUDY DESIGN: Retrospective cohort study. SETTING: Canadian tertiary care center. METHODS: Retrospective chart review of all patients who underwent transoral robotic surgery or transoral laser microsurgery with curative intent for p16+ oropharyngeal SCC and had ≤2 mm initial surgical margins with or without re-resection and adjuvant therapy between January 2019 and December 2023. The primary outcome was 2-year locoregional control. RESULTS: In total, 80 patients were included; 54 had a 2-year follow-up available. Initial margins were close (≤2 mm) in 44 patients (55%) and positive in 34 patients (42.5%). Re-resection was performed in 67 patients (83.8%). Following re-resections, three patients (3.8%) had positive margins and 77 (96.2%) had close or clear margins. Deep margins were most involved (67.5%). In total, 42 patients (52.5%) received adjuvant (chemo)-radiotherapy. Amongst the 54 patients eligible for 2-year follow-up, one had regional recurrence at 18 months postsurgery, resulting in a 2-year locoregional control rate of 98.1%. CONCLUSION: p16+ oropharyngeal SCC with close initial surgical margins results in low locoregional recurrence rates. Intraoperative re-resection with acceptance of close surgical margins may be an effective strategy to preserve both oncologic safety and 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 | 0.002 |
| 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".