Evidence map›Paper›PMID 33685437›Full record

ReviewBMC surgery2021

Supporting laparoscopic general surgery training with digital technology: The United Kingdom and Ireland paradigm.

Gemma Humm, Rhiannon L Harries, Danail Stoyanov, Laurence B Lovat

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BMC surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 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

12 citing papers in PubMed, 1 synthesis or guideline 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

4 authors.

Gemma HummWellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, Charles Bell House, 43-45 Foley Street, London, W1W 7TY, UK. g.humm@ucl.ac.uk.ORCID http://orcid.org/0000-0001-9962-5462
Rhiannon L HarriesSwansea Bay University Health Board, Swansea, Wales, UK.
Danail StoyanovWellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, Charles Bell House, 43-45 Foley Street, London, W1W 7TY, UK.
Laurence B LovatWellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, Charles Bell House, 43-45 Foley Street, London, W1W 7TY, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Surgical training in the UK and Ireland has faced challenges following the implementation of the European Working Time Directive and postgraduate training reform. The health services are undergoing a digital transformation; digital technology is remodelling the delivery of surgical care and surgical training. This review aims to critically evaluate key issues in laparoscopic general surgical training and the digital technology such as virtual and augmented reality, telementoring and automated workflow analysis and surgical skills assessment. We include pre-clinical, proof of concept research and commercial systems that are being developed to provide solutions. Digital surgical technology is evolving through interdisciplinary collaboration to provide widespread access to high-quality laparoscopic general surgery training and assessment. In the future this could lead to integrated, context-aware systems that support surgical teams in providing safer surgical care.

Indexed as

Digital TechnologyGeneral SurgeryLaparoscopyHumansIrelandUnited KingdomArtificial intelligenceDigital technologyLaparoscopic surgeryNontechnical skillsSimulationSurgical trainingTelementoringVirtual reality mobile application

Identifiers

PMID33685437
PMCPMC7941971

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.