Impact of neoadjuvant radiation on margins for non–squamous cell carcinoma sinonasal malignancies
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Treatment of non-squamous cell carcinoma sinonasal malignancies (NSCCSMs) typically involves surgery and radiotherapy (RT), but optimal sequencing remains controversial. STUDY DESIGN: Retrospective chart review. METHODS: Patients with NSCCSM treated with combined surgery and RT between 2000 and 2011 were identified. Margin control, overall survival, disease-free survival, local recurrence-free survival, and regional recurrence-free survival were compared between neoadjuvant and adjuvant RT groups. RESULTS: Eight-four patients were included (23 neoadjuvant and 61 adjuvant RT). A higher proportion of patients receiving neoadjuvant RT achieved negative/close resection margins compared to those receiving adjuvant RT (83% vs. 41%, P = .003). Multivariable analysis also showed that neoadjuvant RT was associated with an 81% decreased odds of positive margins odds ratio: 0.19, 95% confidence interval: 0.05-0.77, P = .02). CONCLUSIONS: Neoadjuvant RT may be associated with improved margin status among patients with NSCCSM treated with surgery and RT. Future prospective studies with larger, more homogeneous populations are needed to clarify optimal treatment strategies. LEVEL OF EVIDENCE: 4 Laryngoscope, 128:2796-2803, 2018.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".