Review of the efficacy and safety of cryoablation for the treatment of small renal masses.
Bibliographic record
Abstract
PURPOSE: Small renal masses are increasingly being discovered incidentally on imaging for another reason. The standard of care of these masses involves excision by open or laparoscopic techniques. Recently, ablative techniques, such as radiofrequency ablation (RFA) and cryoablation, have taken a more prominent role in the treatment algorithm of these masses. We evaluate the effectiveness and safety of cryoablation to treat renal tumours. METHODS: A review of the literature was conducted. There was no language restriction. Studies were obtained from the following sources: the Cochrane Library, PUBMED, EMBASE and LILACS. RESULTS: There was no clinical trial identified in the literature. Thus, we described the results from 23 case series and retrospective studies with a reasonable sample size (number of reported patients in each study ≥30), with a total of 2104 analyzed tumours from 2038 patients. There was wide variability in the outcomes reported, but success rates were generally good. Follow-up was generally short, but some series reported outcomes at 5 years. The most common complications reported were hemorrhage (some of the patients requiring transfusion), perinephric hematoma and urine leaks. CONCLUSION: Cryoablation presents a feasible treatment for patients with small renal masses. Only short-term data are available and, as such, meaningful conclusions regarding long-term cancer control cannot be made. More rigorous studies are needed.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".