Standard of Care and New Operative Techniques for Small Renal Tumors: a Meta-analysis with a Special Focus on Cryoablation
Bibliographic record
Abstract
Introduction: the aim of this article is to realize a meta-analysis of published data evaluating open nephrectomy, laparoscopic nephrectomy and cryoablation for small renal masses to define the current data, and to adjust oncological results for patients and follow-up heterogeneity by performing econometric estimations. Materials and methods: a systematic literature review using the STROBE checklist was performed for clinically localized sporadic renal masses from the beginning of January 1996 until October 31, 2008. The main variables evaluated were patients’ age and sex, tumor size, ASA score, duration of follow-up, available clinical outcomes, pathological data, and oncological outcomes. Results: 152 studies representing 19,994 patients were analyzed. The authors found a significant lower operation time for percutaneous cryoablation, and a lower hospital stay and blood losses for all types of cryoablation (i.e. open, laparoscopic and percutaneous). No significant difference is found between cryoablation and resection methods as regard to complication rates. When adjusting oncological results for patients and follow-up heterogeneity, higher recurrence rates at five years are found for cryoablation, on the contrary, no difference is found for specific-cancer survival rates at five years. Conclusions: cryoablation is a safe and less invasive procedure than resection methods. However, its long term efficacy has not yet been established and a more stringent selection of patients is needed to reduce recurrence rates.
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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 | 0.029 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.048 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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, 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".