Management of renal cell carcinoma in transplant kidney: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: After transplantation, approximately 10% of renal cell carcinomas are detected in graft kidneys. These tumors (gRCC) present surgeons with the difficulty of finding a treatment that guarantees both oncological clearance and maintenance of function. We conducted a systematic review and an individual patient data meta-analysis on the oncology, safety and functional outcomes of the available treatments for gRCC. EVIDENCE ACQUISITION: , Kaplan-Meier, Log-rank and Standard Cox regression and other tests were used to compare treatments. Studies' quality was evaluated using a modified version of Newcastle Ottawa Scale. EVIDENCE SYNTHESIS: A number of 29 studies (357 patients) were included. No differences between TA and PN were found in terms of safety, functional and oncological outcomes for T1a gRCCs. When applied to pT1b gRCC, PN showed no difference in complications, progression or cancer-specific deaths compared to smaller lesions; PN validity for pT2 gRCCs should be considered unverified due to lack of sufficient evidence. The efficacy and safety of PN or TA for multiple gRCC remain controversial. In case of non-functioning, large (T≥2), complicated or metastatic gRCCs, GN appears to be the most reasonable choice. Quality of evidence ranged from very low to moderate. Studies with large cohorts and longer follow-up are still needed to clarify oncological and functional differences. CONCLUSIONS: PN and TA might be offered as a nephron-sparing treatment in patients with T1a gRCC. There is no significant difference between these options and GN in terms of oncological outcomes and complications. PN and TA offer similar functional outcomes and graft preservation. PN for T1b gRCC seems feasible and safe, but its validity should be considered unverified.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.023 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".