Evidence map›Paper›PMID 27549358›Full record

ReviewNature reviews. Urology2016

Simulation-based training and assessment in urological surgery.

Abdullatif Aydin, Nicholas Raison, Muhammad Shamim Khan, Prokar Dasgupta, Kamran Ahmed

Registry-linked trialAbstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Urology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03863028 (Development and Validation of a Simulator-based Test in Transurethral Resection of Bladder Tumors), which is not on this map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
12.2field-weighted citation impact, top 1% 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.

NCT03863028 completednot on this mapstarted 2018, after this paper: background citation

Development and Validation of a Simulator-based Test in Transurethral Resection of Bladder Tumors

TypeobservationalSponsorRigshospitalet, DenmarkRan2018 to 2018Enrolled49ConditionsValidity-evidence for a Simulator-based Test, Virtual Simulation, Bladder Cancer, Surgical Skill TrainingArmsSimulation training
3 · Its place in the literature

Who cites it

42 citing papers in PubMed, 1 synthesis or guideline pooled it, 117 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. 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 at 2 institutions in 1 country.

Abdullatif AydinMRC Centre for Transplantation, King's College London, 5th Floor Southwark Wing, Guy's Hospital, London SE1 9RT, UK.
Nicholas RaisonMRC Centre for Transplantation, King's College London, 5th Floor Southwark Wing, Guy's Hospital, London SE1 9RT, UK.
Muhammad Shamim KhanMRC Centre for Transplantation, King's College London, 5th Floor Southwark Wing, Guy's Hospital, London SE1 9RT, UK.
Prokar DasguptaMRC Centre for Transplantation, King's College London, 5th Floor Southwark Wing, Guy's Hospital, London SE1 9RT, UK.
Kamran AhmedMRC Centre for Transplantation, King's College London, 5th Floor Southwark Wing, Guy's Hospital, London SE1 9RT, UK.
Guy's Hospital · GBKing's College Hospital · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Simulation has become widely accepted as a supplementary method of training. Within urology, the greatest number of procedure-specific models and subsequent validation studies have been carried out in the field of endourology. Many generic-skills simulators have been created for laparoscopic and robot-assisted surgery, but only a limited number of procedure-specific models are available. By contrast, open urological simulation has only seen a handful of validated models. Of the available modalities, virtual reality (VR) simulators are most commonly used for endourology and robotic surgery training, the former also employing many high-fidelity bench models. Smaller dry-lab and ex vivo animal models have been used for laparoscopic and robotic training, whereas live animals and human cadavers are widely used for full procedural training. Newer concepts such as augmented-reality (AR) models and patient-specific simulators have also been introduced. Several curricula, including one recommended within, have been produced, incorporating various different training modalities and nontechnical skills training techniques. Such curricula and validated models should be used in a structured fashion to supplement operating room training.

Indexed as

Clinical CompetenceAnimalsHumansLaparoscopyRobotic Surgical ProceduresUrologic Surgical ProceduresUrologyUser-Computer Interface

Identifiers

PMID27549358
OpenAlexW2509202003

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.