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Record W4403335933 · doi:10.3390/jpm14101053

Optimizing Urological Concurrent Robotic Multisite Surgery: Juxtaposing a Single-Center Experience and a Literature Review

2024· review· en· W4403335933 on OpenAlexaboutno aff
Rafał B. Drobot, Marcin Lipa, Weronika Anna Zahorska, Daniel Ludwiczak, Artur A. Antoniewicz

Bibliographic record

VenueJournal of Personalized Medicine · 2024
Typereview
Languageen
FieldMedicine
TopicUreteral procedures and complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRobotic surgeryProstatectomyNephrectomyMEDLINECochrane LibraryBlood lossSurgeryMedical physicsGeneral surgeryRandomized controlled trialProstate cancerInternal medicineKidney

Abstract

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Introduction: This article juxtaposes case series with a systematic review to evaluate the feasibility, safety, and clinical outcomes of concurrent robotic multisite urological surgeries, specifically robot-assisted radical prostatectomy (RARP) and robot-assisted partial nephrectomy (RAPN), for synchronous prostate and kidney cancers. Aim: The aims of this study were to evaluate the feasibility, safety, and clinical outcomes of urological concurrent robotic multisite surgeries through a comparison of institutional findings with the existing literature. Materials and Methods: A retrospective analysis was conducted on eight institutional cases of concurrent robotic multisite surgeries performed between 2021 and 2024. The primary outcomes measured were operative time, blood loss, and postoperative complications. A systematic review of the literature was performed, searching PubMed, Embase, and Cochrane Library databases, with the last search conducted on 1 July 2024. Studies were included if they reported on concurrent robotic surgeries corresponding to the procedures performed at the institution, including RARP with RAPN, RARP with robotic transabdominal preperitoneal inguinal hernia repair (RTAPPIHR), and other multisite robotic surgeries. Risk of bias was assessed using the modified Newcastle–Ottawa Scale. Descriptive statistics were used to analyze operative time and blood loss, with confidence intervals (CIs) calculated to assess precision. Categorical variables, including postoperative complications, were summarized using frequencies and percentages. Heterogeneity was assessed using the I2 statistic, with values above 50% indicating substantial heterogeneity. A random effects model was applied when necessary, and sensitivity analyses excluded studies with high risk of bias. Results: We describe a unique docking technique employed in our procedures, which allows for atraumatic transitions between surgeries using the same port sites. Our institutional cases demonstrated the feasibility and safety of concurrent robotic multisite surgery, with a mean operative time of 315 min (95% CI: 290–340) and mean blood loss of 300 mL (95% CI: 250–350). There were no significant intraoperative complications reported. These findings are consistent with the literature, where mean operative times range from 390 to 430 min and blood loss ranges from 200 to 330 mL. Notably, no positive surgical margins or declines in postoperative renal function were observed in our cases. The systematic review included nine retrospective studies involving 40 cases of concurrent RARP and RAPN, as well as eleven studies including 392 cases of RARP combined with RTAPPIHR. The findings from these studies support the feasibility and safety of concurrent surgeries, showing similar rates of operative time, blood loss, and postoperative complications. Conclusions: Concurrent robotic multisite surgeries, such as RARP combined with RAPN or RTAPPIHR, appear to be safe and feasible. Our data suggest these procedures are non-inferior to separate surgeries in terms of safety and complication rates. Potential benefits, including reduced operative times, shorter hospital stays, and more efficient resource use, may translate into cost savings, although no formal cost-effectiveness analysis was conducted. Limitations include the small sample size, retrospective design, and lack of long-term follow-up. Prospective trials are needed to validate these findings and further refine the techniques. Funding: this review did not receive any external funding. Registration: this review was not registered in any public protocol registry due to its comparative retrospective nature.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.682
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.131
GPT teacher head0.405
Teacher spread0.274 · 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 designOther design
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

Citations3
Published2024
Admission routes1
Has abstractyes

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