Evidence map›Paper›PMID 41479879›Full record

ArticleOrthopaedic journal of sports medicine2025

3D Patellar instability Anatomical Severity Score (3D-PASS): A Novel Machine Learning Score Using 3D Bone Position From MRI to Predict Outcomes of Patellar Instability Treatment (Using a Subset of Data in the JUPITER Cohort).

Marissa L Sinopoli, Anthony A Gatti, Christian E Wright, Anna Bartsch, Matthew W Veerkamp, Robert D Boutin, Douglas N Mintz, Kathleen H Emery, Kevin G Shea, Akshay S Chaudhari and 10 more

Abstract read
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In one paragraph

Article in Orthopaedic journal of sports medicine, 2025. 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

20 authors.

Marissa L SinopoliDepartment of Mechanical Engineering, Stanford University, Stanford, CA, USA; Department of Engineering, Harvey Mudd College, Claremont, CA, USA.ORCID https://orcid.org/0000-0002-4469-6113
Anthony A GattiDepartment of Radiology, Stanford University, Stanford, California, USA.
Christian E WrightDepartment of Biology, Stanford University, Stanford, California, USA; School of Medicine, Washington University, Saint Louis, Missouri, USA.ORCID https://orcid.org/0009-0007-7818-3352
Anna BartschDepartment of Orthopaedic Surgery, Stanford University Medical Center, Stanford, California, USA; Department of Orthopedics and Traumatology, University Hospital Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0003-4309-3019
Matthew W VeerkampDivision of Pediatric Orthopaedics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Robert D BoutinDepartment of Radiology, Stanford University, Stanford, California, USA.
Douglas N MintzDepartment of Radiology & Imaging, Hospital for Special Surgery, New York, USA.
Kathleen H EmeryDepartment of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Kevin G SheaDepartment of Orthopaedic Surgery, Stanford University Medical Center, Stanford, California, USA.ORCID https://orcid.org/0000-0002-4160-0163
Akshay S ChaudhariDepartment of Radiology, Stanford University, Stanford, California, USA.
JUPITER Study Group
Eric J WallDivision of Orthopaedic Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Stacey ElangovanDepartment of Radiology and Medical Imaging, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Daniel W GreenDepartment of Pediatric Orthopedics, Hospital for Special Surgery, New York, USA.
Peter D FabricantDepartment of Pediatric Orthopedics, Hospital for Special Surgery, New York, USA.
Sabrina M StricklandDepartment of Orthopedic Surgery, Hospital for Special Surgery, New York, USA.
Beth E Shubin SteinDepartment of Sports Medicine, Hospital for Special Surgery, New York, USA.
Shital N ParikhDivision of Pediatric Orthopaedics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Scott L DelpDepartment of Mechanical Engineering, Stanford University, Stanford, California, USA; Department of Orthopaedic Surgery, Stanford University Medical Center, Stanford, California, USA; Department of Bioengineering, Stanford University, Stanford, California, USA.ORCID https://orcid.org/0000-0002-9643-7551
Seth L ShermanDepartment of Orthopaedic Surgery, Stanford University Medical Center, Stanford, California, USA.

Funding

TR&D Project 3: OpenSim for PredictionP41EB027060 · NIBIB · STANFORD UNIVERSITY · PI SCOTT L DELP · 2020 to 2026
$9.7M
NIBIB NIH HHS P41 EB027060
6 · The paper itself

Abstract

Background: Patellar instability treatment outcomes vary. Early prediction of patient responses to treatment is needed to personalize interventions, reduce recurrent instability, and improve outcomes. Existing scores that predict outcomes rely on 2-dimensional (2D) imaging measures that do not fully capture 3-dimensional (3D) anatomical data. Purpose/Hypothesis: We aimed to develop a 3D medical imaging-based anatomical score of patellar instability severity. It was hypothesized that our 3D score would demonstrate stronger associations with instability history and posttreatment patient-reported outcomes (PROs) than a 2D imaging score. Study Design: Cohort study (diagnosis); Level of evidence, 2. Methods: Pretreatment magnetic resonance images from 26 control patients and 244 patients with first-time and recurrent patellar instability were retrospectively analyzed from 2 sites associated with the prospective JUPITER (Justifying Patellar Instability Treatment by Results) study group. Using statistically derived 3D features that reflect relative bone positions and bone shape, as well as 2D imaging measures, we developed several preliminary anatomical severity scores. We tested these scores based on the ability to distinguish first-time from recurrent instability patients. Then, we assessed correlations between these scores and baseline and 1-year posttreatment PROs, using the Kujala Anterior Knee Pain Scale (Kujala) and Banff Patellar Instability Instrument 2.0 (BPII 2.0). A final score-the 3D Patellar instability Anatomical Severity Score (3D-PASS)-was created to best correlate with PROs. Results: 3D-PASS, based on patellar and tibial positions relative to the femur, distinguished first-time from recurrent instability patients ( Conclusion: A higher 3D-PASS is associated with instability history and worse outcomes across all patients (first-time and recurrent) treated nonoperatively or operatively. While 3D relative bone positions correlated with outcomes, 3D bone shapes did not.

Indexed as

3-dimensional analysisanatomymachine learningmagnetic resonance imagingpatellar instabilitypatient-reported outcome

Identifiers

PMID41479879
PMCPMC12754041

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.