Evidence map›Paper›PMID 33544636›Full record

ArticleThe British journal of radiology2021

Longitudinal study of the morphological and T2* changes of knee cartilages of marathon runners using prototype software for automatic cartilage segmentation.

Ping Zhang, Baohai Yu, Ranxu Zhang, Xiaoshuai Chen, Shuying Shao, Yan Zeng, Jianling Cui, Jian Zhao

Open access · bronzeAbstract read
In one paragraph

Article in The British journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 pooled it
2.7field-weighted citation impact, top 10% 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

10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Ping ZhangDepartment of Radiology, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, China.
Baohai YuDepartment of Radiology, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, China.
Ranxu ZhangDepartment of Radiology, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, China.
Xiaoshuai ChenDepartment of Radiology, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, China.
Shuying ShaoDepartment of Radiology, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, China.
Yan ZengDepartment of Radiology, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, China.
Jianling CuiDepartment of Radiology, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, China.
Jian ZhaoDepartment of Radiology, The Third Hospital of Hebei Medical University, Hebei Province Biomechanical Key Laboratory of Orthopedics, Shijiazhuang, China.
Hebei Medical University · CNThird Hospital of Hebei Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo study the effect of long-distance running on the morphological and T2* assessment of knee cartilage.

methods3D-DESS and T2* mapping was performed in 12 amateur marathon runners (age: between 21 and 37 years) without obvious morphological cartilage damage. MRI was performed three times: within 24 h before the marathon, within 12 h after the marathon, and after a period of convalescence of two months. An automatic cartilage segmentation method was used to quantitatively assessed the morphological and T2* of knee cartilage pre- and post-marathon. The cartilage thickness, volume, and T2* values of 21 sub-regions were quantitatively assessed, respectively.

resultsThe femoral lateral central (FLC) cartilage thickness was increased when 12-h post-marathon compared with pre-marathon. The tibial medial anterior (TMA) cartilage thickness was decreased when 2 months post-marathon compared with pre-marathon. The tibial lateral posterior (TLP) cartilage volume was increased when 12-h post-marathon compared with pre-marathon. The cartilage T2* value in most sub-regions had the upward trend when 12-h post-marathon and restored trend when 2 months post-marathon, compared with pre-marathon. The femoral lateral anterior (FLA) and TMA cartilage volumes were decreased 2 months post-marathon compared with pre-marathon.

conclusionsThe marathon had some effects on the thickness, volume, and T2* value of the knee cartilages. The thickness and volume of knee cartilage in most sub-regions were without significantly changes post-marathon compared with pre-marathon. T2* value of knee cartilage in most sub-regions was increased right after marathon and recovered 2 months later. The TLP and TMA subregions needed follow-up after marathon. ADVANCES IN KNOWLEDGE: The morphological and T2* changes of knee cartilage after marathon were evaluated by MRI and automatic segmentation software. This study was the first to use cartilage automatic segmentation software to evaluate the effects of marathon on the morphology and biochemical components of articular cartilage, and to predict the most vulnerable articular cartilage subregions, for the convenience of future exercise adjustment and the avoidance of sports cartilage injury.

Indexed as

AdultCartilage, ArticularFemaleHumansImage Processing, Computer-AssistedKnee JointLongitudinal StudiesMagnetic Resonance ImagingMaleMarathon RunningYoung Adult

Identifiers

PMID33544636
PMCPMC8011266
OpenAlexW3128791677

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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.