Preoperative sarcopenia status is associated with lymphovascular invasion in upper tract urothelial carcinoma patients treated with radical nephroureterectomy
Bibliographic record
Abstract
INTRODUCTION: Sarcopenia is a novel concept representing skeletal muscle wasting and has been identified as a prognostic factor for several cancers. The aims of this study were to evaluate the prognostic significance of sarcopenia and the relationship between sarcopenia and poor pathological findings in upper tract urothelial carcinoma (UTUC) patients who underwent radical nephroureterectomy (RNU). METHODS: We identified 123 UTUC patients who underwent RNU between 2003 and 2014. We assessed sarcopenia by measuring the area of skeletal muscle at the third lumber vertebra on preoperative computed tomography scans. Sarcopenia was classified based on a sex-specific consensus definition. We investigated whether sarcopenia predicts clinical outcomes, such as cancer death and poor pathological findings at RNU. RESULTS: A total of 50 (40.7%) patients had sarcopenia. In a multivariate Cox regression analysis, sarcopenia was not associated with cancer-specific survival (CSS), and lymphovascular invasion (LVI) (hazard ratio 5.88; p=0.002) was the only independent risk factor for CSS. A multivariate logistic regression analysis showed that sarcopenia independently correlated with the LVI status (odds ratio 2.36; p=0.025). LVI was positive in 27 of 50 (54%) and 25 of 73 (34%) patients with and without sarcopenia, respectively (p=0.029). CONCLUSIONS: Preoperative sarcopenia predicted the LVI status, which was a strong prognostic factor for UTUC patients after RNU.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".