Evidence map›Paper›PMID 39440074›Full record

ArticleProceedings (Baylor University. Medical Center)2024

A single-center retrospective review of laparoscopic totally extraperitoneal versus robotic transabdominal preperitoneal inguinal hernia repair.

Bryana Baginski, Daniel Tran, Gerald Ogola, David Arnold

Abstract read
In one paragraph

Article in Proceedings (Baylor University. Medical Center), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 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

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Review
  5. 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

4 authors.

Bryana BaginskiBaylor University Medical Center, Dallas, Texas, USA.ORCID https://orcid.org/0000-0001-7916-6908
Daniel TranBaylor University Medical Center, Dallas, Texas, USA.
Gerald OgolaBaylor University Medical Center, Dallas, Texas, USA.
David ArnoldBaylor University Medical Center, Dallas, Texas, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: With the increased use of robotic surgery, robotic transabdominal preperitoneal repair (R-TAPP) has become a commonly used approach for inguinal hernia repair. The laparoscopic totally extraperitoneal repair (L-TEP) has the advantage of not entering the peritoneal cavity; however, it has greater technical difficulty. Robotic surgery has demonstrated superiority over laparoscopy in many surgical settings, but there is limited evidence comparing L-TEP and R-TAPP. Methods: This was a retrospective review of patients who underwent L-TEP and R-TAPP at Baylor University Medical Center between December 2011 and January 2022. Information on patient demographic characteristics, comorbidities, postoperative complications, hospital length of stay, and postoperative complications requiring a procedure was collected. Results: A total of 298 patients were analyzed; 245 underwent R-TAPP and 53 underwent L-TEP. Hernia recurrence was significantly decreased in those who underwent R-TAPP (1.2%) compared to L-TEP (9.4%) ( Conclusions: With the transition from L-TEP to R-TAPP over recent years, there is limited evidence supporting this change in practice. Our single-center retrospective review demonstrates that R-TAPP is noninferior to L-TEP and has significantly decreased hernia recurrence.

Indexed as

Hernia recurrenceinguinal hernia repairlaparoscopic totally extraperitoneal inguinal hernia repairpostoperative painrobotic transabdominal preperitoneal inguinal hernia repair

Identifiers

PMID39440074
PMCPMC11492716

What OpenQuestion holds

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Read underepoch 390

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.