Evidence map›Paper›PMID 38427363›Full record

ArticleJournal of musculoskeletal & neuronal interactions2024

Precision Errors and Monitoring Time Interval in Pediatric Muscle Imaging and Neuromuscular Performance Assessment.

Yuwen Zheng, Joel L Lanovaz, James J D Johnston, Saija A Kontulainen

Abstract read
In one paragraph

Article in Journal of musculoskeletal & neuronal interactions, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Yuwen ZhengCollege of Kinesiology, University of Saskatchewan, Canada.
Joel L LanovazCollege of Kinesiology, University of Saskatchewan, Canada.
James J D JohnstonCollege of Engineering, University of Saskatchewan, Canada.
Saija A KontulainenCollege of Kinesiology, University of Saskatchewan, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo determine precision errors and monitoring time intervals in imaged muscle properties and neuromuscular performance, and to explore growth-related factors associated with precision errors in children.

methodsWe included 35 children (mean age 10.5yrs) in the precision study cohort and 40 children (10.7yrs) in the follow-up study cohort. We assessed forearm and lower leg muscle properties (area, density) with peripheral quantitative computed tomography. We measured neuromuscular performance via maximal pushup, grip force, countermovement and standing long jump force, power, and impulse along with long jump length. We calculated precision errors (root-mean-squared coefficient of variation) from the precision cohort and monitoring time intervals using annual changes from the follow-up cohort. We explored associations between precision errors (coefficient of variation) and maturity, time interval (between repeated measures), and anthropometric changes using Spearman's rank correlation (p<0.05).

resultsMuscle measures exhibited precision errors of 1.3-14%. Monitoring time intervals were 1-2.6yrs, except muscle density (>43yrs). We identified only one association between precision errors and maturity (maximal pushup force: rho=-0.349; p=0.046).

conclusionsImaging muscle properties and neuromuscular performance measures had precision errors of 1-14% and appeared suitable for follow-up on ~2yr scales (except muscle density). Maximal pushup force appeared more repeatable in mature children.

Indexed as

Bone DensityMusclesChildFollow-Up StudiesHumansLegMuscle StrengthTomography, X-Ray ComputedGrowthMusclePediatricsPeripheral Quantitative Computed Tomography

Identifiers

PMID38427363
PMCPMC10910199

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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.