Evidence map›Paper›PMID 37305642›Full record

ArticleTranslational andrology and urology2023

Multi-institutional feasibility and safety outcomes of retroperitoneal robot-assisted partial nephrectomy in morbidly obese patients.

Thomas Edward Stout, Ian Mitchell McElree, Aaron Christopher Smith, Goran Rac, Hiten Patel, Gopal Gupta, Paul Thomas Gellhaus

Open access · diamondAbstract read
In one paragraph

Article in Translational andrology and urology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Thomas Edward StoutDepartment of Urology, University of Iowa, Iowa City, IA, USA.
Ian Mitchell McElreeDepartment of Urology, University of Iowa, Iowa City, IA, USA.
Aaron Christopher SmithDepartment of Urology, University of Iowa, Iowa City, IA, USA.
Goran RacDepartment of Urology, Loyola University Medical Center, Chicago, IL, USA.
Hiten PatelDepartment of Urology, Loyola University Medical Center, Chicago, IL, USA.
Gopal GuptaDepartment of Urology, Loyola University Medical Center, Chicago, IL, USA.
Paul Thomas GellhausDepartment of Urology, University of Iowa, Iowa City, IA, USA.
University of Iowa · USLoyola University Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Robotic-assisted partial nephrectomy (RAPN) is an established treatment modality for small renal masses. While retroperitoneal RAPN (rRAPN) has the benefit of avoiding the peritoneal cavity and provides more direct access to the renal hilum and posterior kidney, there is concern about the feasibility of rRAPN particularly in morbidly obese [body mass index (BMI) ≥40 kg/m Methods: A retrospective review of a cohort of morbidly obese patients who underwent rRAPN at two academic institutions was performed. Patient characteristics, operative data, and postoperative complication rates were assessed. Results: A total of 22 morbidly obese patients were included for analysis, with a median follow-up duration of 52 months. Median patient age was 61 years and median BMI was 44.9 kg/m Conclusions: rRAPN in select morbidly obese patients appears to have acceptable operative and postoperative outcomes. Further studies and follow-up are needed to better generalization and understand long-term impacts.

Indexed as

Partial nephrectomyrenal massretroperitonealrobotic surgery

Identifiers

PMID37305642
PMCPMC10251096
OpenAlexW4380290523

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.