Evidence map›Paper›PMID 36515819›Full record

ArticleJournal of robotic surgery2023

Parallel, component training in robotic total mesorectal excision.

Deena Harji, Nour Aldajani, Thomas Cauvin, Alexander Chauvet, Quentin Denost

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of robotic surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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

  1. Pooled it
  2. European Society of Coloproctology guideline on training in robotic colorectal surgery (2024).Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2024
    Guideline
  3. Training and credentialing in robotic general surgery.International journal of colorectal disease · 2026
    Review
  4. Article
  5. Review
  6. Article
  7. 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

5 authors.

Deena HarjiDepartment of Digestive Surgery, Colorectal Unit, Haut-Lévêque Hospital, Bordeaux University Hospital, Pessac, France.
Nour AldajaniDepartment of Digestive Surgery, Colorectal Unit, Haut-Lévêque Hospital, Bordeaux University Hospital, Pessac, France.
Thomas CauvinDepartment of Digestive Surgery, Colorectal Unit, Haut-Lévêque Hospital, Bordeaux University Hospital, Pessac, France.
Alexander ChauvetDepartment of Digestive Surgery, Colorectal Unit, Haut-Lévêque Hospital, Bordeaux University Hospital, Pessac, France.
Quentin DenostDepartment of Digestive Surgery, Colorectal Unit, Haut-Lévêque Hospital, Bordeaux University Hospital, Pessac, France. q.denost@bordeaux-colorectal-institute.fr.ORCID http://orcid.org/0000-0003-3088-5244

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There has been widespread adoption of robotic total mesorectal excision (TME) for rectal cancer in recent years. There is now increasing interest in training robotic novice surgeons in robotic TME surgery using the principles of component-based learning. The aims of our study were to assess the feasibility of delivering a structured, parallel, component-based, training curriculum to surgical trainees and fellows. A prospective pilot study was undertaken between January 2021 and May 2021. A dedicated robotic training pathway was designed with two trainees trained in parallel per each robotic case based on prior experience, training grade and skill set. Component parts of each operation were allocated by the robotic trainer prior to the start of each case. Robotic proficiency was assessed using the Global Evaluative Assessment of Robotic Skills (GEARS) and the EARCS Global Assessment Score (GAS). Three trainees participated in this pilot study, performing a combined number of 52 TME resections. Key components of all 52 TME operations were performed by the trainees. GEARS scores improved throughout the study, with a mean overall baseline score of 17.3 (95% CI 15.1-1.4) compared to an overall final assessment mean score of 23.8 (95% CI 21.6-25.9), p = 0.003. The GAS component improved incrementally for all trainees at each candidate assessment (p < 0.001). Employing a parallel, component-based approach to training in robotic TME surgery is safe and feasible and can be used to train multiple trainees of differing grades simultaneously, whilst maintaining high-quality clinical outcomes.

Indexed as

Rectal NeoplasmsRoboticsRobotic Surgical ProceduresClinical CompetenceHumansPilot ProjectsProspective StudiesRectal cancerRobotic surgery training

Identifiers

What OpenQuestion holds

Textmetadata
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.