Evidence map›Paper›PMID 38831919›Full record

ArticleMini-invasive surgery2023

Robotic-assisted approach for complex cholecystectomies.

Gina Zhu, Kelli Ann Ifuku, Kimberly S Kirkwood

Abstract read
In one paragraph

Article in Mini-invasive surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Gina ZhuDepartment of Surgery, University of California, Genentech Hall, San Francisco, CA 94158, USA.
Kelli Ann IfukuDepartment of Surgery, University of California, Genentech Hall, San Francisco, CA 94158, USA.
Kimberly S KirkwoodDepartment of Surgery, University of California, Genentech Hall, San Francisco, CA 94158, USA.

Funding

Surgical Oncology Training GrantT32CA251070 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ESSERMAN, LAURA J · 2020 to 2024
$1.5M
NCI NIH HHS T32 CA251070
6 · The paper itself

Abstract

Robotic approaches have facilitated the minimally invasive completion of increasingly complex surgical procedures. In the management of the difficult gallbladder, we have found that the wristed instruments, three-dimensional camera, the ability to use indocyanine green (ICG) with integrated fluorescent imaging, and ease of intracorporeal suturing to be useful in tackling the challenges associated with complex benign gallbladder disease. We describe the rationale and technical lessons learned during four cases of complex cholecystectomies that highlight the management principles and technical advantages afforded by the use of the robotic-assisted laparoscopic (RAL) approach. The cases include a subtotal fundus-first reconstituting cholecystectomy, subtotal fenestrating cholecystectomy, a cholecystocolonic fistula managed by a RAL subtotal fenestrating cholecystectomy, and an iatrogenic cholecystoduodenal fistula managed by RAL cholecystectomy. In each case, the operation was performed safely without intraoperative injury or conversion to open, and three of the four patients were discharged from the recovery room. In our view, these favorable outcomes were greatly facilitated by the robotic platform. It is our intent to share adaptations and innovations that we found helpful to encourage other surgeons with sufficient robotic experience to tackle complex gallbladder cases minimally invasively.

Indexed as

complex cholecystectomyindocyanine greenRobotic-assisted laparoscopic cholecystectomy

Identifiers

PMID38831919
PMCPMC11146680

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.