Evidence map›Paper›PMID 36757529›Full record

ArticleInsights into imaging2023

Inter-vendor and inter-observer reliability of diffusion tensor imaging in the musculoskeletal system: a multiscanner MR study.

Vito Chianca, Domenico Albano, Stefania Rizzo, Mario Maas, Luca Maria Sconfienza, Filippo Del Grande

Abstract read
In one paragraph

Article in Insights into imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 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

6 authors.

Vito ChiancaClinica di Radiologia EOC IIMSI, Lugano, Switzerland. vitochianca@gmail.com.ORCID http://orcid.org/0000-0002-2279-5559
Domenico AlbanoIRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
Stefania RizzoClinica di Radiologia EOC IIMSI, Lugano, Switzerland.
Mario MaasDepartment of Radiology and Nuclear Medicine, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands.
Luca Maria SconfienzaIRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
Filippo Del GrandeClinica di Radiologia EOC IIMSI, Lugano, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo evaluate the inter-observer and inter-vendor reliability of diffusion tensor imaging parameters in the musculoskeletal system.

methodsThis prospective study included six healthy volunteers three men (mean age: 42; range: 31-52 years) and three women (mean age: 36; range: 30-44 years). Each subject was scanned using different 3 Tesla magnetic resonance scanners from three different vendors at three different sites bilaterally. First, the intra-class correlation coefficient was used to determine between-observers agreement for overall measurements and clinical sites. Next, between-group comparisons were made through the nonparametric Friedman's test. Finally, the Bland-Altman method was used to determine agreement among the three scanner measurements, comparing them two by two.

resultsA total of 792 measurement were calculated. ICC reported high levels of agreement between the two observers. ICC related to MD, FA, and RD measurements ranged from 0.88 (95% CI 0.85-0.90) to 0.95 (95% CI 0.94-0.96), from 0.85 (95% CI 0.81-0.88) to 0.95 (95% CI 0.93-0.96), and from 0.89 (0.85-0.90) to 0.92 (0.90-0.94). No statistically significant inter-vendor differences were observed. The Bland-Altmann method confirmed a high correlation between parameter values.

conclusionAn excellent inter-observer and inter-vendor reliability was found in our study.

Indexed as

Diffusion tensor imagingMagnetic resonanceMuscleReliabilityReproducibility

Identifiers

PMID36757529
PMCPMC9911574

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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.