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Record W2559249142 · doi:10.1016/j.arthro.2016.08.025

Three‐Dimensional Magnetic Resonance Imaging Quantification of Glenoid Bone Loss Is Equivalent to 3‐Dimensional Computed Tomography Quantification: Cadaveric Study

2016· article· en· W2559249142 on OpenAlexaff
Adam B. Yanke, Jason J. Shin, I. Y. Pearson, Bernard R. Bach, Anthony A. Romeo, Brian J. Cole, Nikhil N. Verma

Bibliographic record

VenueArthroscopy The Journal of Arthroscopic and Related Surgery · 2016
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsUniversity of SaskatchewanRoyal University Hospital
Fundersnot available
KeywordsCadaveric spasmMagnetic resonance imagingMedicineNuclear medicineTomographySoft tissueComputed tomographyRadiologyBiomedical engineeringAnatomy

Abstract

fetched live from OpenAlex

PURPOSE: To assess the ability of 3-dimensional (3D) magnetic resonance imaging (MRI, 1.5 and 3 tesla [T]) to quantify glenoid bone loss in a cadaveric model compared with the current gold standard, 3D computed tomography (CT). METHODS: Six cadaveric shoulders were used to create a bone loss model, leaving the surrounding soft tissues intact. The anteroposterior (AP) dimension of the glenoid was measured at the glenoid equator and after soft tissue layer closure the specimen underwent scanning (CT, 1.5-T MRI, and 3-T MRI) with the following methods (0%, 10%, and 25% defect by area). Raw axial data from the scans were segmented using manual mask manipulation for bone and reconstructed using Mimics software to obtain a 3D en face glenoid view. Using calibrated Digital Imaging and Communications in Medicine images, the diameter of the glenoid at the equator and the area of the glenoid defect was measured on all imaging modalities. RESULTS: In specimens with 10% or 25% defects, no difference was detected between imaging modalities when comparing the measured defect size (10% defect P = .27, 25% defect P = .73). All 3 modalities demonstrated a strong correlation with the actual defect size (CT, ρ = .97; 1.5-T MRI, ρ = .93; 3-T MRI, ρ = .92, P < .0001). When looking at the absolute difference between the actual and measured defect area, no significance was noted between imaging modalities (10% defect P = .34, 25% defect P = .47). The error of 3-T 3D MRI increased with increasing defect size (P = .02). CONCLUSIONS: Both 1.5- and 3-T-based 3D MRI reconstructions of glenoid bone loss correlate with measurements from 3D CT scan data and actual defect size in a cadaveric model. Regardless of imaging modality, the error in bone loss measurement tends to increase with increased defect size. Use of 3D MRI in the setting of shoulder instability could obviate the need for CT scans. CLINICAL RELEVANCE: The goal of our work was to develop a reproducible method of determining glenoid bone loss from 3D MRI data and hence eliminate the need for CT scans in this setting. This will lead to decreased cost of care as well as decreased radiation exposure to patients. The long-term goal is a fully automated system that is as approachable for clinicians as current 3D CT technology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.288
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations60
Published2016
Admission routes1
Has abstractyes

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