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Cryoablation vs radiofrequency ablation for the treatment of renal cell carcinoma: a meta‐analysis of case series studies

2012· review· en· W2151731573 on OpenAlexaff
Regina El Dib, Naji J. Touma, Anil Kapoor

Bibliographic record

VenueBritish Journal of Urology · 2012
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsQueen's UniversityMcMaster University
Fundersnot available
KeywordsCryoablationMedicineRadiofrequency ablationNephrectomyRenal cell carcinomaLaparoscopyCryosurgerySurgeryCryotherapyComplicationAblationKidney cancerRadiologyGeneral surgeryKidneyOncologyInternal medicine

Abstract

fetched live from OpenAlex

UNLABELLED: Study Type - Therapy (systematic review). Level of Evidence 2b What's known on the subject? and What does the study add? The oncological success of partial nephrectomy in the treatment of small renal masses is well established. However, partial nephrectomy has largely supplanted the radical approach. In the last decade, laparoscopy has been adopted as the new surgical approach for the treatment of renal cell carcinoma. Laparoscopy offers the advantage of lower analgesic use, shorter hospital stay, and quicker recovery time. More recently, ablative technologies have been investigated as an alternative to laparoscopic partial nephrectomy. These techniques can often be performed percutaneously in the radiology suite, or laparoscopically without the need for hilar clamping. However, only the cryoablation and radiofrequency ablation modalities have had widespread use with several series reporting short to intermediate results. This review shows that both cryoablation and radiofrequency ablation are promising therapies in patients with small renal tumours (<4 cm), who are considered poor candidates for more involved surgery. OBJECTIVE: • To determine the current status of the literature regarding the clinical efficacy and complication rates of cryoablation vs radiofrequency ablation in the treatment of small renal tumours. METHODS: • A review of the literature was conducted. There was no language restriction. Studies were obtained from the following sources: MEDLINE, EMBASE and LILACS. • Inclusion criteria were (i) case series design with more than one case reported, (ii) use of cryoablation or radiofrequency ablation, (iii) patients with renal cell carcinoma and, (iv) outcome reported as clinical efficacy. • When available, we also quantified the complication rates from each included study. • Proportional meta-analysis was performed on both outcomes with a random-effects model. The 95% confidential intervals were also calculated. RESULTS: • Thirty-one case series (20 cryoablation, 11 radiofrequency ablation) met all inclusion criteria. • The pooled proportion of clinical efficacy was 89% in cryoablation therapy from a total of 457 cases. There was a statistically significant heterogeneity between these studies showing the inconsistency of clinical and methodological aspects. • The pooled proportion of clinical efficacy was 90% in radiofrequency ablation therapy from a total of 426 cases. There was no statistically significant heterogeneity between these studies. • There was no statistically significant difference regarding complications rate between cryoablation and radiofrequency ablation. CONCLUSIONS: • This review shows that both ablation therapies have similar efficacy and complication rates. • There is urgency for performing clinical trials with long-term data to establish which intervention is most suitable for the treatment of small renal masses.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0130.020
Bibliometrics0.0060.009
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.137
GPT teacher head0.351
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations219
Published2012
Admission routes1
Has abstractyes

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