Evidence map›Paper›PMID 34522987›Full record

ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2022

Magnetic resonance imaging does not reliably detect Kaplan fiber injury in the setting of anterior cruciate ligament tear.

Thomas B Lynch, Jeremy M Bernot, David J Oettel, Douglas Byerly, Volker Musahl, Jesse Chasteen, Ivan J Antosh, Jeanne C Patzkowski, Andrew J Sheean

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Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

6 citing papers in PubMed, 13 citations in OpenAlex.

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4 · The record

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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

9 authors at 3 institutions in 1 country.

Thomas B LynchSan Antonio Military Medical Center, 3551 Roger Brooke Road, San Antonio, TX, 78234, USA. tblynch@gwu.edu.ORCID http://orcid.org/0000-0003-4970-8088
Jeremy M BernotSan Antonio Military Medical Center, 3551 Roger Brooke Road, San Antonio, TX, 78234, USA.
David J OettelSan Antonio Military Medical Center, 3551 Roger Brooke Road, San Antonio, TX, 78234, USA.
Douglas ByerlySan Antonio Military Medical Center, 3551 Roger Brooke Road, San Antonio, TX, 78234, USA.
Volker MusahlUniversity of Pittsburgh, Pittsburgh, PA, USA.
Jesse ChasteenUniformed Health Services University, Bethesda, MD, USA.
Ivan J AntoshSan Antonio Military Medical Center, 3551 Roger Brooke Road, San Antonio, TX, 78234, USA.
Jeanne C PatzkowskiSan Antonio Military Medical Center, 3551 Roger Brooke Road, San Antonio, TX, 78234, USA.
Andrew J SheeanSan Antonio Military Medical Center, 3551 Roger Brooke Road, San Antonio, TX, 78234, USA.
Brooke Army Medical Center · USUniformed Services University of the Health Sciences · USUniversity of Pittsburgh · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThere has been a continued effort to better understand the role Kaplan fiber injury plays in persistent instability following ACL tears. However, the prevalence of these injuries remains poorly understood. Therefore, the purpose of this study was to define the prevalence of Kaplan fiber injury in the setting of complete anterior cruciate ligament tear using a commonly used grading system for assessing ligament injuries. The inter-rater reliability of this commonly used grading system and the relationship between Kaplan fiber injury and injury to other structures commonly found in conjunction with ACL tears was also evaluated.

methodsAll isolated, complete anterior cruciate ligament tears confirmed on magnetic resonance imaging within 90 days of injury between 2014 and 2020 at a single institution were included for analysis. Each scan was read by two, fellowship-trained musculoskeletal radiologists. Kaplan fiber injury was evaluated using a previously described grading scheme. Kappa, [Formula: see text], of inter-rater agreement was determined for all magnetic resonance image scans. Kruskal Wallis test was performed to assess for associations between Kaplan fiber injury and magnet strength (1.5 T vs. 3.0 T), patient gender, the presence of medial and/or lateral meniscal tears, and/or posterolateral tibial bone bruise.

resultsBetween 2014 and 2020, 131 patients (94 males, 37 females) with a complete anterior cruciate ligament tear were included in the final analysis. The mean age of the cohort was 27.8 ± 6.8 years. Kaplan fiber injuries were identified in 51 of 131 (38.9%, CI 31.0-47.5%) scans with complete anterior cruciate ligament injuries (Grade 1: 28, Grade 2: 18, and Grade 3: 5). Inter-rater agreement for Kaplan fiber injury was fair ([Formula: see text] with 43 (32.8%) scans requiring third reviewer adjudication. There were no significant associations between Kaplan fiber injury and gender, magnet strength, meniscal tears, or posterolateral tibial bone bruise.

conclusionThe prevalence of Kaplan fiber injuries was comparable to previously described rates; however, the classification system used to report Kaplan fiber injury was associated with low inter-rater reliability. The presence of Kaplan fiber injury was not associated with other injuries commonly observed in conjunction with ACL tear. The previously proposed Kaplan fiber injury classification system is not reproducible nor is it likely to aid surgeons in distinguishing higher grades of rotatory knee instability. LEVEL OF EVIDENCE: Level IV.

Indexed as

Anterior Cruciate Ligament InjuriesAnterior Cruciate Ligament ReconstructionCartilage DiseasesContusionsTibial Meniscus InjuriesAdultFemaleHumansMagnetic Resonance ImagingMaleReproducibility of ResultsRetrospective StudiesYoung AdultACLKaplan fibersKneeMRI

Identifiers

PMID34522987
OpenAlexW3201277919

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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.