Evidence map›Paper›PMID 34338140›Full record

ArticleInternational biomechanics2021

Detection of knee wobbling as a screen to identify athletes who may be at high risk for ACL injury.

Akino Aoki, Satoshi Kubota, Kosuke Morinaga, Naiquan Nigel Zheng, Shangcheng Sam Wang, Kazuyoshi Gamada

Abstract read
In one paragraph

Article in International biomechanics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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.

Akino AokiInternational University of Health and Welfare, Graduate School of Medical Technology and Health Welfare Science, Hiroshima International University, Hiroshima, Chiba, Japan.
Satoshi KubotaTokyo International University, Saitama, Japan.
Kosuke MorinagaInternational University of Health and Welfare, Graduate School of Medical Technology and Health Welfare Science, Hiroshima International University, Hiroshima, Chiba, Japan.
Naiquan Nigel ZhengDepartment of Mechanical Engineering and Engineering Science, University of North Carolina at Charlotte, Atrium Health, NC, USA.
Shangcheng Sam WangDepartment of Mechanical Engineering and Engineering Science, University of North Carolina at Charlotte, Atrium Health, NC, USA.
Kazuyoshi GamadaInternational University of Health and Welfare, Graduate School of Medical Technology and Health Welfare Science, Hiroshima International University, Hiroshima, Chiba, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study developed a method to detect knee wobbling (KW) at low knee flexion. KW consists of quick uncontrollable medio-lateral knee movements without knee flexion, which may indicate a risk of ACL injury. Ten female athletes were recorded while performing slow, single-leg squats. Using motion capture data, the ratio of the frontal angular velocity to sagittal angular velocity (F/S) was calculated. An 'F/S spike' was defined when the F/S ratio exceeded 100%. The number of F/S spikes was counted before and after low-pass filtering at different cut-off frequencies. Intraclass correlation coefficients for KW and filtered F/S spike were analysed. KWs per squat cycle showed a median (range) of 3 (2-8) times. F/S spikes before and after low-pass filtering at 3-, 6-, 10-, and 15-Hz were 51 (12-108), 2 (0-6), 3 (1-12), 5 (2-21), and 9 (3-33) times, respectively. KWs and F/S spikes on motion capture with 6-Hz, low-pass filtering were well correlated (

Indexed as

Anterior Cruciate Ligament InjuriesAthletesBiomechanical PhenomenaFemaleHumansKneeKnee Jointknee kinematicsknee wobblingSingle-leg squat

Identifiers

PMID34338140
PMCPMC8330762

What OpenQuestion holds

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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.