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Record W4416553375 · doi:10.7759/cureus.97538

Off-Clamp Versus On-Clamp Robot-Assisted Partial Nephrectomy: A Meta-Analysis of Clinical Trials and Matched Cohort Studies

2025· article· en· W4416553375 on OpenAlexaboutno aff
Abdelrahman Abdelhameed, Ahmad Ali, Salma Ahmed, Farzana Haque, Aranee Thirukketheesparan, Maria Khan, M. El-Attar, Mohamed Hesham Gamal

Bibliographic record

VenueCureus · 2025
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsObservational studyNephrectomyRandomized controlled trialRenal functionMeta-analysisFunnel plotSystematic reviewPublication biasCohort study

Abstract

fetched live from OpenAlex

Kidney cancer incidence continues to rise globally, with partial nephrectomy established as the standard of care for localized renal masses. Robot-assisted partial nephrectomy has become the preferred approach, with debate continuing regarding the optimal management of renal blood flow during tumor excision. While on-clamp techniques involve temporary renal artery occlusion to minimize blood loss, off-clamp approaches avoid ischemic injury by maintaining continuous renal perfusion. This meta-analysis aimed to compare perioperative, functional, and oncological outcomes between off-clamp and on-clamp robot-assisted partial nephrectomy. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, searching PubMed, Web of Science, Scopus, and Cochrane databases. Twenty studies, comprising two randomized controlled trials and 18 observational studies, were included, totaling 4,961 patients after matching. Quality assessment utilized the Newcastle-Ottawa Scale for observational studies and the Cochrane Risk of Bias 2 tool for randomized trials. Meta-analysis employed risk ratios (RRs) for dichotomous outcomes and mean differences (MDs) for continuous variables, with random-effects models applied when heterogeneity exceeded 50%. Publication bias was assessed using funnel plots and Egger's regression test. Off-clamp robot-assisted partial nephrectomy demonstrated significantly lower major complication rates compared to on-clamp techniques (RR: 1.66, p = 0.018). Renal function preservation favored the off-clamp approach, with better postoperative glomerular filtration rate (MD: -3.45 mL/min/1.73 m2, p < 0.0001). The on-clamp technique showed less estimated blood loss (MD: -32.7 mL, p < 0.0001), though this did not translate to differences in transfusion requirements (RR: 0.80, p = 0.208). Operative time was longer for off-clamp procedures (MD: 21.55 minutes, p = 0.030). Hospital stay duration did not differ significantly between techniques (MD: 0.02 days, p = 0.946). Subgroup analysis by follow-up duration revealed that renal function benefits were most pronounced at intermediate (three to nine months) and long-term (≥12 months) follow-up. Our study concluded that off-clamp robot-assisted partial nephrectomy offers superior safety, with significantly reduced major complications and better preservation of renal function, particularly at intermediate and long-term follow-up, despite requiring longer operative time. Both techniques demonstrate equivalent oncological efficacy with comparable positive surgical margins. The off-clamp approach should be preferred for patients at high risk of ischemic injury. At the same time, technique selection should be individualized based on patient renal reserve, tumor complexity, and surgical expertise.

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)0.022
metaresearch head score (Gemma)0.042
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: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.042
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0180.051
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.525
GPT teacher head0.525
Teacher spread0.000 · 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
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

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