Long-Term Oncologic Outcomes of Off-Clamp Robotic Partial Nephrectomy for Cystic Renal Tumors: A Propensity Score Matched-Pair Comparison of Cystic versus Pure Clear Cell Carcinoma
Bibliographic record
Abstract
Few data are available on survival outcomes of partial nephrectomy performed for cystic renal tumors. We present the first long-term oncological outcomes of cystic (cystRCC) versus pure clear cell renal cell carcinoma (ccRCC) in a propensity score-matched (PSM) analysis. Our “renal cancer” prospectively maintained database was queried for “cystRCC” or “ccRCC” and “off-clamp robotic partial nephrectomy” (off-C RPN). The two groups were compared for age, gender, tumor size, pT stage, and Fuhrman grade. A 1:3 PSM analysis was applied to reduce covariate imbalance to <10% and two homogeneous populations were generated. Student t- and Chi-square tests were used for continuous and categorical variables, respectively. Ten-year oncological outcomes were compared between the two cohorts using log-rank test. Univariable Cox regression analysis was used to identify predictors of disease progression after RPN. Out of 859 off-C RPNs included, 85 cases were cystRCC and 774 were ccRCC at histologic evaluation. After applying the PSM analysis, two cohorts were selected, including 64 cystRCC and 170 ccRCC. Comparable 10-year cancer-specific survival probability (95.3% versus 100%, p = 0.146) was found between the two cohorts. Conversely, 10-year disease-free survival probability (DFS) was less favorable for pure ccRCC than cystRCC (66.69% versus 90.1%, p = 0.035). At univariable regression analysis, ccRCC histology was the only independent predictor of DFS probability (HR 2.96 95% CI 1.03–8.47, p = 0.044). At the 10-year evaluation, cystRCC showed favorable oncological outcomes after off-C RPN. Pure clear cell variant histology displayed a higher rate of disease recurrence than cystic lesions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".