Surgical Margin Definition and Assessment in Head and Neck Oncology: A Cross-Sectional Survey of Canadian Head and Neck Surgeons
Bibliographic record
Abstract
IMPORTANCE: Head and neck squamous cell carcinomas (HNSCC) are responsible for a significant amount of morbidity and mortality in Canada. Surgical margins are one of the most important factors used to guide treatment; however, currently there is a lack of consensus on the ideal surgical margin definition, sampling, and assessment method. OBJECTIVE: To understand the current perspectives and practice patterns of Canadian head and neck surgeons with respect to surgical margin: (1) definition, (2) sampling, (3) pathological assessment. DESIGN: A 24-question cross-sectional survey was sent via email through the Canadian Society of Otolaryngology-Head & Neck Surgery (CSOHNS), and responses were gathered from December 19, 2023, to March 12, 2024. Responses were aggregated and reported using descriptive statistics. SETTING/PARTICIPANTS: The survey was conducted in Canada among self-reported staff head and neck oncology surgeons with membership in the CSOHNS. RESULTS: A total of 36 staff head and neck oncology surgeons responded from across Canada. The most common (58.3%) definition of a negative surgical margin for oral cavity HNSCC was ">5 mm formalin fixed paraffin embedded distance." To obtain surgical margins, surgeons were split with 44.1% using only a tumor bed approach and 32.4% using only a specimen-driven approach. A dedicated head and neck pathologist is always available more commonly for final pathological assessment (63.6%) versus intraoperative frozen section assessment (15.5%). Finally, most surgeons reported having a synoptic standardized reporting system for annotating margin status (78.8%). CONCLUSIONS/RELEVANCE: The results of this survey provide a current-state analysis of head and neck surgeons across Canada and set the stage for future efforts to be directed toward standardizing the collection method and reporting criteria for surgical margins in HNSCC.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".