Evidence map›Paper›PMID 41582969›Full record

ReviewStroke (Hoboken, N.J.)2022

Stroke Neurointervention: A Novel Educational Pathway to Improve Neurology Resident Training in Neurointervention and Regional Access to Thrombectomy.

Neil C Suryadevara, Aravind Reddy, Deborah Bradshaw, Grahame C Gould, Hesham E Masoud

Abstract readReview
In one paragraph

Review in Stroke (Hoboken, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Neil C SuryadevaraDepartment of Neurology State University of New York Upstate Medical University Syracuse NY.
Aravind ReddyDepartment of Neurology State University of New York Upstate Medical University Syracuse NY.
Deborah BradshawDepartment of Neurology State University of New York Upstate Medical University Syracuse NY.
Grahame C GouldDepartment of Neurosurgery State University of New York Upstate Medical University Syracuse NY.
Hesham E MasoudDepartment of Neurology State University of New York Upstate Medical University Syracuse NY.ORCID https://orcid.org/0000-0002-2796-1499

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large vessel occlusions account for ≈30% of acute ischemic stroke. Mechanical thrombectomy is a highly beneficial treatment for large vessel occlusion strokes in appropriately selected patients. However, there is significant geographic variability in regional access to this important therapy, and broadening coverage by using non-neuroscience specialties places patients at risk for suboptimal cerebrovascular care. An alternative solution is to train more neurologists to perform this procedure and consolidate expertise in vascular neurology. However, neurology residents have minimal exposure to the angiography suite and are disadvantaged in this regard when it comes to preparedness for fellowship training. We detail a novel training pathway incorporating neuroendovascular training into the adult neurology residency and an option to continue hybridized stroke-neurointervention training in a vascular neurology fellowship. Here, we present the development of the training curriculum, early trainee experience, and challenges to widespread implementation. The stroke-neurointervention training pathway was created with the intention of improving access to this important therapy by enfolding early training into neurology residency.

Indexed as

educationinterventionalneurologyresidencystrokethrombectomytraining

Identifiers

PMID41582969
PMCPMC12778845

What OpenQuestion holds

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LicenceCC BY-NC
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.