Predictors of Progression-Free Survival and Overall Survival in Metastatic Non-Clear Cell Renal Cell Carcinoma: A Single-Center Experience
Bibliographic record
Abstract
BACKGROUND: Due to the infrequency of non-clear cell renal cell carcinoma (RCC), there is currently a paucity of high-quality literature to help guide the effective treatment of these tumors. Recently, biomarkers such as platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), systemic immune inflammation (SII) index and C-reactive protein to albumin ratio (CAR) have been demonstrated to be closely related to poor prognosis of patients with RCC. The objective of this study was to evaluate these biomarkers for determining the progression-free survival (PFS) and overall survival (OS) in patients with metastatic non-clear cell cancer. METHODS: We retrospectively reviewed 31 cases diagnosed with metastatic non-clear cell RCC from January 2012 to December 2017. We assessed the prognostic value (OS and PFS) of pretreatment PLR, LMR, SII index and CAR based on multivariate analysis and Kaplan-Meier survival curve. RESULTS: Median time of OS and PFS were 15.5 months (95% confidence interval (CI): 13.7 - 15.2) and 10.9 months (95% CI: 8.9 - 12.8), respectively. The median PFS (0.001) and OS (P = 0.01) was shorter in patients with PLR > 171, LMR < 2.61. Moreover, median PFS but not OS was significantly lower in SII index > 883 (P = 0.064) and CAR > 0.11 (P = 0.229). Scan to surgery time (3.91 weeks, P = 0.001) was also significantly related to progression. CONCLUSIONS: Elevated pretreatment inflammatory biomarkers such as PLR, LMR, SII index and CAR are significant determinants of shorter PFS and OS (PLR and LMR only) in patients with metastatic non-clear cell RCC treated with cytoreductive nephrectomy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".