The MUSES∗: a prognostic study on 1360 patients with sinonasal cancer undergoing endoscopic surgery-based treatment
Bibliographic record
Abstract
BACKGROUND: Over the last 2 decades, transnasal endoscopic surgery (TES) has become the most frequently employed surgical technique to treat sinonasal malignancies. The rarity and heterogeneity of sinonasal cancers have hampered large non-population-based analyses. METHODOLOGY: All patients receiving TES-including treatment between 1995 and 2021 in 5 referral hospitals were included. A prognostic study was performed, and multivariable models were transformed into nomograms. Training and validation sets were based on results from 3 European and 2 non-European centres, respectively. RESULTS: The training and validation set included 940 and 420 patients, respectively. The mean age at surgery, primary-versus-recurrent presentation, histology distribution, type of surgery, T category and type of adjuvant treatment were differently distributed in the training and validation set. In the training set, 5-year overall survival and recurrence-free survival with a 95%-confidence interval were 72.7% (69.5-76.0%) and 66.4% (63.1-69.8%), respectively, significantly varying with histology. At multivariable analyses, age, gender, previous treatment, the extent of resection on the cranial, lateral and posterolateral axes, grade/subtype, T category, nodal status, margin status and adjuvant treatment were all associated with different prognostic outcomes, displaying a heterogeneous significance and effect size according to histology. The internal and external validation of nomograms was satisfactory (optimism-corrected C-index >0.7 and cumulative area under curve >0.7) for all histologies but mucosal melanoma. CONCLUSIONS: Outcomes of TES-based treatment of sinonasal cancers vary substantially with histology. This large, non-population-based study provides benchmark data on the prognosis of sinonasal cancers that are deemed suitable for treatment including TES.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".