Ablative Therapies versus Partial Nephrectomy for Small Renal Masses – A systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Introduction: The ideal treatment of small renal masses is unclear. Ablative therapies (AT) have been considered as a potential alternative to partial nephrectomy (PN) due to their lower complication rates and similar oncological durability. We conducted a systematic review to compare oncological outcomes in T1a or T1b patients undergoing AT vs PN. Methods: This review is registered on PROSPERO (CRD42020199099). Medline, EMBASE, and Cochrane CENTRAL were searched to identify studies comparing AT and PN. The Cochrane RoB 2.0, ROBINS-I tool and the GRADE approach were used to assess any risk of biases. Results: From 1,748 identified records, 32 observational studies and 1 RCT involving 74,946 patients were included. AT patients were found to be significant older than PN patients (MD 5.70, 95% CI 3.83- 7.58), which highlights the serious confounding bias found in the included studies. Patients who received AT for T1a tumours were found to have significantly worse overall survival (HR 1.64, 95% CI 1.39-1.95), but similar cancer-specific survival (CSS), metastatic-free survival, and disease-free survival to PN. There were significantly fewer post-operative complications (RR 0.72, 95%CI 0.55- 0.94) and smaller decline in renal function post-operatively in AT (MD: -7.42, 95%CI -13.1- -1.70). In T1b patients, while CSS was similar between AT and PN, there is contradicting evidence for other oncological outcomes. Conclusion: AT is potentially non-inferior to PN in the treatment of T1a small renal masses due to similar long-term oncological durability, lower complication rates and better renal function preservation. In T1b patients, long-term high-quality studies are needed to confirm potential benefits of AT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.026 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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, 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".