Evidence map›Paper›PMID 39452043›Full record

ReviewBrain sciences2024

Neurosurgical Microvascular Anastomosis: Systematic Review of the Existing Simulators and Proposal of a New Training Classification System.

Lelio Guida, Martina Sebök, Marcelo Magaldi Oliveira, Christiaan Hendrik Bas van Niftrik, Fady T Charbel, Marco Cenzato, Luca Regli, Giuseppe Esposito

Abstract readReview
In one paragraph

Review in Brain sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Lelio GuidaDepartment of Pediatric Neurosurgery, Assistance Pubilque Hôpitaux de Paris, Hôpital Necker Enfants Malades, Université de Paris Cité, 75015 Paris, France.
Martina SebökDepartement of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, 8091 Zurich, Switzerland.ORCID 0000-0002-7246-3421
Marcelo Magaldi OliveiraDepartment of Surgery, School of Medicine, Federal University of Minas Gerais, Belo Horizonte 31270-901, MG, Brazil.
Christiaan Hendrik Bas van NiftrikDepartement of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, 8091 Zurich, Switzerland.ORCID 0000-0003-0930-8717
Fady T CharbelDepartment of Neurosurgery, University of Illinois at Chicago, Chicago, IL 60612, USA.
Marco CenzatoDepartment of Neurosurgery, Niguarda Great Metropolitan Hospital of Milan, 20162 Milan, Italy.
Luca RegliDepartement of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, 8091 Zurich, Switzerland.ORCID 0000-0003-4639-4474
Giuseppe EspositoDepartement of Neurosurgery, Clinical Neuroscience Center, University Hospital of Zurich, University of Zurich, 8091 Zurich, Switzerland.ORCID 0000-0001-8687-3109

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe literature lacks a combined analysis of neurosurgical microvascular anastomosis training models. We performed a systematic literature search to provide an overview of the existing models and proposed a classification system based on the level of simulation and reproducibility of the microvascular anastomosis.

methodsThe systematic literature search followed the PRISMA guidelines. We consulted MEDLINE, Web of Knowledge, and EMBASE independently for papers about bypass training models. Every training model was analyzed according to six tasks supposed to esteem their fidelity to the real operative setting by using a scoring system from zero to two. Finally, authors classified the models into five classes, from A to E, by summing the individual scores.

resultsThis study included 109 papers for analysis. Training models were grouped into synthetic tubes, ex vivo models (animal vessels, fresh human cadavers, human placentas) and in vivo simulators (live animals-rats, rabbits, pigs). By applying the proposed classification system, live animals and placentas obtained the highest scores, falling into class A (excellent simulators). Human cadavers and animal vessels (ex vivo) were categorized in class B (good simulators), followed by synthetic tubes (class C, reasonable simulators).

conclusionsThe proposed classification system helps the neurosurgeon to analyze the available training models for microvascular anastomosis critically, and to choose the most appropriate one according to the skills they need to improve.

Indexed as

micro-anastomosismicrosurgical trainingmicrovascular surgeryneurosurgical bypass

Identifiers

PMID39452043
PMCPMC11505727

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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.