Evidence map›Paper›PMID 37020985›Full record

ArticlePlastic and reconstructive surgery. Global open2023

Utilization of a 3D Printed Simulation Training Model to Improve Microsurgical Training.

Luke Geoghegan, Dimitrios Papadopoulos, Nicola Petrie, Isabel Teo, Theodora Papavasiliou

Abstract read
In one paragraph

Article in Plastic and reconstructive surgery. Global open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Results of a Pilot Virtual Microsurgery Course for Plastic Surgeons in LMICs.Plastic and reconstructive surgery. Global open · 2024
    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.

Luke GeogheganDepartment of Plastic Surgery, Oxford University Hospitals, John Radcliffe Hospital, Headington, Oxford, UK.
Dimitrios PapadopoulosDepartment of Plastic and Reconstructive Surgery, Queen Victoria Hospital NHS Foundation Trust, East Grinstead, UK.
Nicola PetrieDepartment of Plastic Surgery, Oxford University Hospitals, John Radcliffe Hospital, Headington, Oxford, UK.
Isabel TeoDepartment of Plastic Surgery, Oxford University Hospitals, John Radcliffe Hospital, Headington, Oxford, UK.
Theodora PapavasiliouDepartment of Plastic Surgery, Oxford University Hospitals, John Radcliffe Hospital, Headington, Oxford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Simulation is integral to the development and maintenance of micro- surgical skills. Several simulation models have been described ranging from bench- top to live animal models. High fidelity models are often burdened by cost and ethical issues limiting widespread implementation. This study aims to determine the feasibility of a microsurgical training platform using the Konjac noodle model. Methods: A prospective cohort study was conducted at our institution. A progressive microsurgical training curriculum was developed. A bespoke three-dimensional printed training platform was produced to enable residents to record training and assessment tasks. Microsurgical skills were blindly assessed before and after completing the training program using the University of Western Ontario Microsurgical Skills Assessment instrument. Results: Plastic surgery residents at various stages of training were recruited (n = 10). A significant improvement in vessel preparation from a pre-training median of 3 (IQR 2 -4) versus a post-training of 4 (IQR 3 -5, Conclusion: The use of the Konjac noodle model and video-based assessment using a three-dimensional printed model is an effective teaching tool that improves resident's microsurgical skills.

Identifiers

PMID37020985
PMCPMC10069837

What OpenQuestion holds

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Registered trials

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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.