Sinonasal Undifferentiated Carcinoma (SNUC): The Alberta experience and literature review
Bibliographic record
Abstract
BACKGROUND: Sinonasal undifferentiated carcinoma (SNUC) is a rare malignancy with often dismal outcomes. This study set to determine provincial and literature-wide survival outcomes based on treatment modality. METHODS: Retrospective chart review of all SNUC patients in the province of Alberta from 1986-2010 was conducted. A review of the literature of SNUC patients was also performed. Patient/tumor characteristics, treatment, and follow-up/survival data were collected. Kaplan-Meier and Cox regression survival analyses were performed. RESULTS: 20 patients were treated for SNUC in Alberta and 140 patients were identified in the literature. Pooled median disease-free survival was 12. 7 months and 5-year survival estimate was 6.25%. Cox-Regression analysis demonstrated an overall survival advantage with multimodality treatments (Log-Rank test: p = 0.015). However, no statistically significant differences in disease-free and overall survival were identified between patients treated with chemoradiation or surgery followed by adjuvant therapy. CONCLUSIONS: Treatment of SNUC remains challenging with poor survival outcomes. There appears to be no statistically significant difference in overall, or disease-free survival between treatment modalities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".