Evidence map›Paper›PMID 25114384›Full record

ArticleThe Indian journal of radiology & imaging2014

Peripheral nerve injury grading simplified on MR neurography: As referenced to Seddon and Sunderland classifications.

Avneesh Chhabra, Shivani Ahlawat, Allan Belzberg, Gustav Andreseik

Open access · diamondAbstract read
In one paragraph

Article in The Indian journal of radiology & imaging, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 2 pooled it
9.4field-weighted citation impact, top 2% of its field
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

63 citing papers in PubMed, 2 syntheses or guidelines pooled it, 213 citations in OpenAlex.

  1. Pooled it
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  8. Peripheral nerve repair: innovations and future directions.Journal of translational medicine · 2026
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3 more citing papers are in PubMed but not listed here.

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 at 4 institutions in 2 countries.

Avneesh ChhabraDepartment of Radiology and Orthopaedic Surgery, University of Texas Southwestern Medical Center, Baltimore, MD, USA.
Shivani AhlawatDepartment of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Allan BelzbergDepartment of Neurosurgery, The Johns Hopkins Hospital, Baltimore, MD, USA.
Gustav AndreseikDepartment of Radiology, University Hospital Zürich, Zürich, Switzerland.
Johns Hopkins Hospital · USJohns Hopkins Medicine · USSouthwestern Medical Center · USUniversity Hospital of Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Seddon and Sunderland classifications have been used by physicians for peripheral nerve injury grading and treatment. While Seddon classification is simpler to follow and more relevant to electrophysiologists, the Sunderland grading is more often used by surgeons to decide when and how to intervene. With increasing availability of high-resolution and high soft-tissue contrast imaging provided by MR neurography, the surgical treatment can be guided following the above-described grading systems. The article discusses peripheral nerve anatomy, pathophysiology of nerve injury, traditional grading systems for classifying the severity of nerve injury, and the role of MR neurography in this domain, with respective clinical and surgical correlations, as one follows the anatomic paths of various nerve injury grading systems.

Indexed as

AxonotmesisinjuryMR neurographyneuroma-in-continuityneurotmesisperipheral nerveSeddonSunderland

Identifiers

PMID25114384
PMCPMC4126136
OpenAlexW2007002853

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.