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Record W4390665472 · doi:10.1089/end.2023.0505

Single-Port <i>vs</i> Multiport Robot-Assisted Partial Nephrectomy: A Meta-Analysis

2024· article· en· W4390665472 on OpenAlexaff
Tuan Thanh Nguyen, Xuan Thai Ngo, Nguyen Xuong Duong, Ryan W. Dobbs, Huy Gia Vuong, David‐Dan Nguyen, Jacob Basilius, Narmina Khanmammadova Onder, Dinno Francis Mendiola, Tien‐Dat Hoang, Dang Nhat Minh Pham, An Nguyen, Tuyet Mai Tran Thi, Ali Sohrab Naushad, Mohammed Shahait, David I. Lee

Bibliographic record

VenueJournal of Endourology · 2024
Typearticle
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineNephrectomyMeta-analysisPerioperativeOdds ratioRenal functionBlood lossConfidence intervalSurgeryUrologyInternal medicineKidney

Abstract

fetched live from OpenAlex

Background:Several centers have reported their experience with single-port robot-assisted partial nephrectomy (SP-RAPN); however, it is uncertain if utilization of this platform represents an improvement in outcomes compared to multiport robot-assisted partial nephrectomy (MP-RAPN). To evaluate this, we performed a meta-analysis to compare the perioperative, oncological, and functional outcomes between SP-RAPN and MP-RAPN. Methods:For relevant articles, three electronic databases, including PubMed, Scopus, and Web of Science, were searched from their inception until January 1, 2023. A meta-analysis has been reported in line with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 and assessing the methodological quality of systematic reviews (AMSTAR) guidelines. The odds ratio (OR) and weighted mean difference (MD) were applied for the comparison of dichotomous and continuous variables with 95% confidence intervals (CI). Results:Of the 374 retrieved abstracts, 29 underwent full-text review, and 8 studies were included in the final analysis, comprising a total cohort of 1007 cases of RAPN (453 SP-RAPN cases and 554 MP-RAPN cases). Compared to MP-RAPN, the SP-RAPN group had a significantly longer ischemia time (MD = 4.6 minutes, 95% CI 2.8 to 6.3, p < 0.001), less estimated blood loss (MD = −12.4 mL, 95% CI −24.6 to −0.3, p = 0.045), higher blood transfusion rate (OR = 2.97, 95% CI 1.33 to 6.65, p = 0.008), and higher postoperative estimated glomerular filtration rate (eGFR) at 6 months (MD = 4.9 mL/min, 95% CI 0.2 to 9.7, p = 0.04). There was no significant difference in other outcomes between the two approaches, including the intraoperative complication, overall postoperative complication, minor postoperative complication (Clavien-Dindo I − II), major postoperative complication (Clavien-Dindo III–V), conversion to radical nephrectomy, pain score on day #1, pain score on discharge, morphine milligram equivalent usage, hospital stay, positive surgical margins, and postoperative eGFR. Conclusions:SP-RAPN represents an emerging technique using a novel platform. Initial studies have demonstrated that SP-RAPN is a safe and feasible approach to performing partial nephrectomy, although with inferior outcomes for ischemia time and blood transfusion rates. Further studies will be necessary to define the best usage of SP-RAPN within the surgeon's armamentarium.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.106
GPT teacher head0.320
Teacher spread0.213 · 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 teacher head, not a consensus.

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

Citations24
Published2024
Admission routes1
Has abstractyes

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