Evidence map›Paper›PMID 33156361›Full record

SynthesisWorld journal of urology2021

Evolving robotic surgery training and improving patient safety, with the integration of novel technologies.

I-Hsuan Alan Chen, Ahmed Ghazi, Ashwin Sridhar, Danail Stoyanov, Mark Slack, John D Kelly, Justin W Collins

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in World journal of urology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 10 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed, 10 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

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

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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

7 authors.

I-Hsuan Alan ChenDivision of Surgery and Interventional Science, Research Department of Targeted Intervention, University College London, London, UK. alan_aries@yahoo.com.tw.ORCID http://orcid.org/0000-0003-2720-7549
Ahmed GhaziDepartment of Urology, Simulation Innovation Laboratory, University of Rochester, New York, USA.
Ashwin SridharDivision of Uro-Oncology, University College London Hospital, London, UK.
Danail StoyanovWellcome/ESPRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK.
Mark SlackUniversity of Cambridge, Cambridge, UK.
John D KellyDivision of Surgery and Interventional Science, Research Department of Targeted Intervention, University College London, London, UK.
Justin W CollinsDivision of Surgery and Interventional Science, Research Department of Targeted Intervention, University College London, London, UK. justin.collins@ucl.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionRobot-assisted surgery is becoming increasingly adopted by multiple surgical specialties. There is evidence of inherent risks of utilising new technologies that are unfamiliar early in the learning curve. The development of standardised and validated training programmes is crucial to deliver safe introduction. In this review, we aim to evaluate the current evidence and opportunities to integrate novel technologies into modern digitalised robotic training curricula.

methodsA systematic literature review of the current evidence for novel technologies in surgical training was conducted online and relevant publications and information were identified. Evaluation was made on how these technologies could further enable digitalisation of training.

resultsOverall, the quality of available studies was found to be low with current available evidence consisting largely of expert opinion, consensus statements and small qualitative studies. The review identified that there are several novel technologies already being utilised in robotic surgery training. There is also a trend towards standardised validated robotic training curricula. Currently, the majority of the validated curricula do not incorporate novel technologies and training is delivered with more traditional methods that includes centralisation of training services with wet laboratories that have access to cadavers and dedicated training robots.

conclusionsImprovements to training standards and understanding performance data have good potential to significantly lower complications in patients. Digitalisation automates data collection and brings data together for analysis. Machine learning has potential to develop automated performance feedback for trainees. Digitalised training aims to build on the current gold standards and to further improve the 'continuum of training' by integrating PBP training, 3D-printed models, telementoring, telemetry and machine learning.

Indexed as

EducationEducation, DistanceGeneral SurgeryHumansInventionsMentoringModels, AnatomicPatient SafetyRobotic Surgical ProceduresUrology3D printed modelsEye trackingMachine learningPatient safetyProficiency-based progressionRobotic-assisted surgerySurgical educationTelementoringTraining

Identifiers

PMID33156361
PMCPMC8405494

What OpenQuestion holds

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LicenceCC BY
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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.