An evaluation of the role of tumor load in cytoreductive nephrectomy
Bibliographic record
Abstract
INTRODUCTION: New radiological tools can accurately provide preoperative three-dimensional spatial assessment of metastatic renal cell carcinoma (RCC). We aimed to determine whether the distribution, volume, shape, and fraction of RCC resected in a cytoreductive nephrectomy associates with survival. METHODS: We retrospectively reviewed 560 patients undergoing cytoreductive nephrectomy, performing a comprehensive volumetric analysis in eligible patients of all detectable primary and metastatic RCC prior to surgery. We used Cox regression analysis to determine the association between the volume, shape, fraction resected, and distribution of RCC and overall survival (OS). RESULTS: There were 62 patients eligible for volumetric analysis, with similar baseline characteristics to the entire cohort, and median survivor followup was 34 months. Larger primary tumors were less spherical, but not associated with different metastatic patterns. Increased primary tumor volume and tumor size, but not the fraction of tumor resected, were associated with inferior survival. The rank of tumors based on unidimensional size did not completely correspond to the rank by primary tumor volume, however, both measurements yielded similar concordance for predicted OS. Larger tumor volume was not associated with a longer postoperative time off treatment. CONCLUSIONS: Primary tumor volume was significant for predicting OS, while the fraction of disease resected did not appear to impact patient outcomes. Although rich in detail, our study is potentially limited by selection bias. Future temporal studies may help elucidate whether the primary tumor shape is associated with tumor growth kinetics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".