Evidence map›Paper›PMID 39228198›Full record

ReviewAnnals of coloproctology2024

Robotic colorectal surgery training: Portsmouth perspective.

Guglielmo Niccolò Piozzi, Sentilnathan Subramaniam, Diana Ronconi Di Giuseppe, Rauand Duhoky, Jim S Khan

Abstract readReview
In one paragraph

Review in Annals of coloproctology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

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

  1. Pooled it
  2. Pooled it
  3. Pre-Procedural Robotic Surgery Curricula: A Systematic Review and Thematic Meta-Synthesis.Annals of surgery open : perspectives of surgical history, education, and clinical approaches · 2026
    Review
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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

5 authors.

Guglielmo Niccolò PiozziDepartment of Colorectal Surgery, Portsmouth Hospitals University NHS Trust, Portsmouth, UK.
Sentilnathan SubramaniamColorectal Surgery Unit, Department of General Surgery, Hospital Kuala Lumpur, Kuala Lumpur, Malaysia.
Diana Ronconi Di GiuseppeDepartment of Colorectal Surgery, Portsmouth Hospitals University NHS Trust, Portsmouth, UK.
Rauand DuhokyDepartment of Colorectal Surgery, Portsmouth Hospitals University NHS Trust, Portsmouth, UK.
Jim S KhanDepartment of Colorectal Surgery, Portsmouth Hospitals University NHS Trust, Portsmouth, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to discuss the principles and pillars of robotic colorectal surgery training and share the training pathway at Portsmouth Hospitals University NHS Trust. A narrative review is presented to discuss all the relevant and critical steps in robotic surgical training. Robotic training requires a stepwise approach, including theoretical knowledge, case observation, simulation, dry lab, wet lab, tutored programs, proctoring (in person or telementoring), procedure-specific training, and follow-up. Portsmouth Colorectal has an established robotic training model with a safe stepwise approach that has been demonstrated through perioperative and oncological results. Robotic surgery training should enable a trainee to use the robotic platform safely and effectively, minimize errors, and enhance performance with improved outcomes. Portsmouth Colorectal has provided such a stepwise training program since 2015 and continues to promote and augment safe robotic training in its field. Safe and efficient training programs are essential to upholding the optimal standard of care.

Indexed as

Artificial intelligenceColorectal surgeryEducationRoboticsSimulation training

Identifiers

PMID39228198
PMCPMC11375233

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.