Evidence map›Paper›PMID 42440126›Full record

ArticleEuropean journal of orthopaedic surgery & traumatology : orthopedie traumatologie2026

ShRed: a machine learning model developed to predict shoulder redislocation.

Mohamed E Mahmoud, Rajapriyian Murugaiyan, Rahul Geetala, Jed Smith, Dimitri A Kessler, Andrew Grainger, Graham Tytherleigh-Strong, Joshua Kaggie, Salma Chaudhury

Abstract read
PubMed Publisher
In one paragraph

Article in European journal of orthopaedic surgery & traumatology : orthopedie traumatologie, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Mohamed E MahmoudDepartment of Orthopaedics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK. mohamed.mahmoud19@nhs.net.
Rajapriyian MurugaiyanDepartment of Orthopaedics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Rahul GeetalaDepartment of Orthopaedics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Jed SmithDepartment of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Dimitri A KesslerDepartment of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Andrew GraingerDepartment of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Graham Tytherleigh-StrongDepartment of Orthopaedics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Joshua KaggieDepartment of Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
Salma ChaudhuryDepartment of Orthopaedics, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis feasibility study aimed to develop and evaluate machine learning models to predict shoulder redislocation using joint-specific imaging characteristics, in addition to traditional demographic variables.

methodsA prospective dataset from a tertiary referral centre was analysed, including cartilage MRI thickness measurements. Six classification algorithms were compared using 10-fold stratified cross-validation as the primary evaluation method. Preprocessing involved one-hot encoding of categorical variables and median imputation for missing values. The dataset was stratified into training (80%) and testing (20%) subsets.

resultsA total of 42 patients (54.8% redislocation rate) were included. Random Forest demonstrated the highest cross-validated accuracy of 79.0% (± 16.1%), precision of 88.3%, recall of 75.0%, and AUC of 0.84, with a 95% confidence interval of 69.0% to 89.0%. Feature importance analysis identified years since first dislocation as the most influential predictor, followed by age at first dislocation and glenoid cartilage thickness.

conclusionThis feasibility study demonstrates that machine learning models can predict shoulder redislocation with moderate accuracy. External validation on larger, multicentre datasets is required.

Indexed as

Machine LearningShoulder DislocationShoulder JointAdultCartilage, ArticularClassification AlgorithmsFeasibility StudiesFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsProspective StudiesRandom ForestMachine learningMRIPredictive modelRandom ForestRecurrent dislocationShoulder instability

Identifiers

PMID42440126

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.