Evidence map›Paper›PMID 39170649›Full record

ArticleIndian journal of orthopaedics2024

Meniscal Lesions in Multi-Ligament Knee Injuries.

David Figueroa, María Loreto Figueroa, Martin Cañas, Alexandra Feuereisen, Francisco Figueroa

Abstract read
In one paragraph

Article in Indian journal of orthopaedics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Can we Predict the Outcomes of Arthroscopic Partial Meniscectomy?Current reviews in musculoskeletal medicine · 2026
    Review
  3. 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

5 authors.

David FigueroaFacultad de Medicina Clínica Alemana, Universidad del Desarrollo, Av Vitacura 5951, Vitacura, Santiago Chile.
María Loreto FigueroaFacultad de Medicina Clínica Alemana, Universidad del Desarrollo, Av Vitacura 5951, Vitacura, Santiago Chile.ORCID 0000-0002-2414-7985
Martin CañasFacultad de Medicina Clínica Alemana, Universidad del Desarrollo, Av Vitacura 5951, Vitacura, Santiago Chile.
Alexandra FeuereisenFacultad de Medicina Clínica Alemana, Universidad del Desarrollo, Av Vitacura 5951, Vitacura, Santiago Chile.
Francisco FigueroaFacultad de Medicina Clínica Alemana, Universidad del Desarrollo, Av Vitacura 5951, Vitacura, Santiago Chile.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Multi-ligament knee injuries (MLKIs) are rare and complex knee lesions and are potentially associated with intra-articular injuries, especially meniscal tears. Understanding the meniscal tear patterns involved in MLKI can help the orthopedic surgeon treat these complex injuries. Objective: The purpose of this study was to describe the incidence, classification, and treatment of meniscal injuries in a cohort of patients with MLKIs and carry out an updated review of the evidence available. Materials and methods: Descriptive retrospective study. Patients with a history of reconstructive surgery for MLKI performed between 2013 and 2023 were included. Informed consent was obtained from all patients included in the study. Patient demographics, magnetic resonance imaging (MRI) study, and operative reports were reviewed. Groups were then formed based on ligament injury patterns. Meniscal tears were identified by MRI and through diagnostic arthroscopy for each patient. The association between meniscal lesions and injury patterns was calculated through Fisher's exact test. Agreement between the presence of meniscal tear on MRI and in diagnostic arthroscopy was measured using the kappa test. The sensitivity and specificity of MRI were calculated. We inferred the presence of a meniscal tear by injury pattern using the Agresti-Coull confidence interval. For the statistical analysis, a significance of 5% and a confidence interval of 95% were considered. Results: Seventy patients with MLKIs were included, with a mean age of 30.69 years (SD 10.65). Forty-seven patients had meniscal lesions (67.1%). Of them, 6 had only medial meniscus tears, 31 had only lateral meniscus tears, and 10 had lesions of both menisci, comprising 57 meniscal lesions in total. An anterior cruciate ligament (ACL) + medial collateral ligament/posteromedial corner (MCL/PMC) was the most common injury pattern (52.86% of all patients). Of these 37 patients, 78.38% had meniscal injuries, and most of them (68.97%) were only lateral meniscus injuries. The odds ratio (OR) of having a meniscal tear when having an ACL + medial-side injury was 4.83 (95% CI; 0.89-26.17). Patients with ACL + lateral-side injury pattern had meniscal tears in 42.86%. The lateral meniscus was involved in 100% of these patients. 62.5% of medial meniscus injuries were treated by meniscal repair, and 37.5% by partial meniscectomy. 58.54% of lateral meniscus injuries were treated by meniscal repair, and 39.02% by partial meniscectomy. Agreement calculated using the kappa test between MRI and diagnostic arthroscopy for medial meniscal lesions was 78.57%, and for lateral meniscal lesions was 84.29%. Conclusion: The ligament injury pattern and the side of the injured collateral ligament influenced the incidence and laterality of meniscal damage. ACL + medial-side injuries were shown to have significantly greater meniscal damage compared to other injury patterns. It is crucial to have a high index of suspicion, obtain a high-quality MRI, and arthroscopically evaluate any possible meniscal lesions in MLKIs.

Indexed as

Anterior cruciate ligamentLateral collateral ligamentMedial collateral ligamentMenisciMeniscus tearMulti-ligament injuriesPosterior cruciate ligamentPosterolateral cornerPosteromedial corner

Identifiers

PMID39170649
PMCPMC11333639

What OpenQuestion holds

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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.