Evidence map›Paper›PMID 41699098›Full record

ReviewSkeletal radiology2026

Evaluation of physeal abnormalities of the knee with MRI.

Diego Jaramillo, Camilo Perdomo-Luna, Ola Kvist

Abstract readReview
PubMed Publisher
In one paragraph

Review in Skeletal radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Diego JaramilloDepartment of Radiology, Hospital for Special Surgery and Weill Cornell Medical College, 535 East 70th Street, New York, NY, 10021, USA. jaramillod@hss.edu.ORCID http://orcid.org/0000-0002-9571-9776
Camilo Perdomo-LunaDepartment of Radiology, Hospital for Special Surgery and Weill Cornell Medical College, 535 East 70th Street, New York, NY, 10021, USA.
Ola KvistDepartment of Radiology, Columbia University Medical Center, 530 West 166th Street, New York, NY, 10032, USA.

Funding

Understanding Skeletal Growth Using Diffusion Tensor Imaging of the Physis and MetaphysisR01HD104720 · NICHD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI JAMBAWALIKAR, SACHIN · 2021 to 2025
$3.1M
NICHD NIH HHS R01 HD104720
6 · The paper itself

Abstract

Longitudinal skeletal growth occurs at the physis by a continuous process of chondrocyte division and hypertrophy. The physes of the distal femur and proximal tibia are the main contributors to skeletal growth, and therefore physeal abnormalities are most consequential in the knee. The normal growth structures are well depicted by conventional MR imaging, which demonstrates the integrity of the physeal cartilage, zone of provisional calcification, and primary spongiosa. Insults to the physeal cartilage including trauma, infection, chronic nonbacterial osteomyelitis, and repeated stress are also well depicted by sequences that show discontinuity of the cartilage and zone of provisional calcification. The dreaded complication of these physeal disturbances is the formation of a physeal bar, a bridge of tissue, generally bone, that joins the epiphyseal and metaphyseal bone and tethers growth. MRI shows the size of the abnormality of the physis as well as its location and depicts the abnormal growth by showing abnormal orientation of the growth recovery lines. Abnormal growth activity can be shown by diffusion tensor imaging tractography and quantified by metrics such as tract volume, apparent diffusion coefficient (ADC) and fractional anisotropy (FA).

Indexed as

Growth PlateKnee JointMagnetic Resonance ImagingHumansGrowth arrestGrowth platePhysis

Identifiers

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.