Evidence map›Paper›PMID 40938384›Full record

ArticleSkeletal radiology2026

Tear location of posterior cruciate ligament tears: introduction and reliability of a magnetic resonance imaging-based classification.

Maximilian M Mueller, Sebastian Conner-Rilk, Vincent J De Lorenzi, Tatiana C Monteleone, Robert J O'Brien, Jelle P van der List, Gregory S DiFelice, Douglas N Mintz

Abstract read
PubMed Publisher
In one paragraph

Article in Skeletal radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Maximilian M MuellerDepartment of Orthopaedic Surgery, Hospital for Special Surgery, NewYork-Presbyterian, Weill Medical College of Cornell University, HSS - East River Professional Building, 523 East 72nd Street, 2nd Floor, New York, NY, 10021, USA.
Sebastian Conner-RilkDepartment of Orthopaedic Surgery, Hospital for Special Surgery, NewYork-Presbyterian, Weill Medical College of Cornell University, HSS - East River Professional Building, 523 East 72nd Street, 2nd Floor, New York, NY, 10021, USA.
Vincent J De LorenziDepartment of Orthopaedic Surgery, Hospital for Special Surgery, NewYork-Presbyterian, Weill Medical College of Cornell University, HSS - East River Professional Building, 523 East 72nd Street, 2nd Floor, New York, NY, 10021, USA.
Tatiana C MonteleoneDepartment of Orthopaedic Surgery, Hospital for Special Surgery, NewYork-Presbyterian, Weill Medical College of Cornell University, HSS - East River Professional Building, 523 East 72nd Street, 2nd Floor, New York, NY, 10021, USA.
Robert J O'BrienDepartment of Orthopaedic Surgery, Hospital for Special Surgery, NewYork-Presbyterian, Weill Medical College of Cornell University, HSS - East River Professional Building, 523 East 72nd Street, 2nd Floor, New York, NY, 10021, USA.
Jelle P van der ListSports Medicine Research Institute, The Ohio State University Wexner Medical Center, Columbus, USA.
Gregory S DiFeliceDepartment of Orthopaedic Surgery, Hospital for Special Surgery, NewYork-Presbyterian, Weill Medical College of Cornell University, HSS - East River Professional Building, 523 East 72nd Street, 2nd Floor, New York, NY, 10021, USA. difeliceg@hss.edu.ORCID http://orcid.org/0000-0002-9559-757X
Douglas N MintzDepartment of Radiology and Imaging, Hospital for Special Surgery, Weill Medical College of Cornell University, New York, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo introduce a tear location classification system for posterior cruciate ligament (PCL) injuries, aiming to unify diagnostic criteria and improve clinical decision-making based on tear location. MATERIALS AND

methodsA retrospective analysis was conducted using magnetic resonance imaging (MRI) examinations from a single institution, identifying all patients with acute or subacute PCL injuries from 2008 to 2024. Ligament sprains without significant fiber disruption and chronic injuries were excluded. Tears were classified by four independent observers according to the relative length (%) of the intact distal remnant compared to the total PCL length: Type I (> 90%), Type Ib (femoral bony avulsion), Type II (90-75%), Type III (75-25%), Type IV (distal tear 10-25%), Type V (< 10%), and Type Vb (tibial bony avulsion). For intra-observer reliability analysis, measurements were repeated once by two observers after 4 weeks.

resultsA subset of 45 MRIs with diagnosed PCL injuries that met the inclusion criteria (mean age 40.5 ± 19.2 years, 40% female) had 24% proximal tears, 33% midsubstance tears, 33% distal tears, and 11% single-bundle injuries. Inter-observer reliability (Fleiss' kappa, 0.88; 95% CI, 0.84-0.94; p < 0.01) and intra-observer reliability demonstrated to be almost perfect (Cohen's kappa, 0.95; 95% CI, 0.86-0.99 & 0.92; 95% CI, 0.84-0.99).

conclusionThe proposed MRI-based classification system-which includes femoral avulsion, proximal quarter, midsubstance (50%), distal quarter, and tibial avulsion injuries-offers a reliable method for identifying the anatomical location of partial and complete PCL injuries. Standardizing tear localization has the potential to improve diagnostic consistency and inform more tailored, evidence-based treatment strategies. LEVEL OF EVIDENCE: Diagnostic study; III.

Indexed as

Knee InjuriesMagnetic Resonance ImagingPosterior Cruciate LigamentAdultFemaleHumansMaleMiddle AgedReproducibility of ResultsRetrospective StudiesRuptureMRI classificationPosterior cruciate ligamentTear location

Identifiers

PMID40938384

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.