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Record W2524051402

TOWARD A MECHANICAL MARKER OF OSTEOARTHRITIS

2014· article· en· W2524051402 on OpenAlexvenueno aff
Jena Shank, Gregor Kuntze, Gulshan Sharma, Jillian E. Beveridge, Janet L. Ronsky

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2014
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisAnterior cruciate ligamentSoft tissueMagnetic resonance imagingWeight-bearingBraceValgusOrthodonticsNuclear medicineSurgeryRadiologyPathology
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Anterior cruciate ligament (ACL) rupture increases an individual’s risk of developing tibiofemoral (TF) osteoarthritis (OA)[1]. Clinical radiographic assessment of OA is insensitive to early changes in TF soft tissue and currently there are no disease modifying treatments available. Given the negative implications of OA on quality of life[2], and large economic impact on the healthcare system[3] new methods for detecting pre-radiographic OA are needed. In vivo TF soft tissue compressive stiffness may be an ideal disease marker. It reflects the dynamic joint response to loading, which has been shown in vitro to deteriorate with OA. The aim of this investigation was to determine the change in TF proximity during loading and estimate in vivo TF compressive stiffness for healthy and ACL deficient (ACLD) subjects.  The hypothesis was that ACLD subjects have lower cartilage stiffness. METHODS Two subjects (S1: Healthy, 34 yrs, 82 kg; S2: ACLD 8 yrs post-injury, 44 yrs, 77.1 kg) volunteered for participation. Subjects were tested in the morning and transferred between imaging locations using a wheelchair to minimize soft tissue loading. High-resolution steady-state fast precision (SSFP) magnetic resonance (MR) scans were obtained for subject knees (General Electric, 3T MR scanner). Thereafter, subjects were transferred to the Dual Fluoroscopy (DF) imaging lab. Subjects were fitted with a custom-made knee brace and led lined apron and thyroid collar. Subjects performed a two-legged standing weight bearing task for ten minutes during which time DF images were acquired at 6Hz for the first 60s and at 30s intervals for minute 2-10. MR data were segmented in Amira (VSG, Germany) to create subject specific 3D bone models. Unloaded cartilage thicknesses were estimated in Amira using the high resolution SSPF scan sequences. DF images were distortion corrected and calibrated using a direct linear transform. 3D bone translations and rotations were reconstructed using 2D-3D registration in Autoscoper (Brown University, USA). Here, the positions of 3D bone models were matched to the two 2D DF x-ray views. TF bone proximities were calculated as the change in the Euclidean distance between the origin of the 3D bone models. Data for the 1 st 30 s of loading were used to estimate TF soft tissue compressive stiffness. RESULTS TF cartilage thickness (unloaded) and compressive stiffness values (loaded) are summarized in Table 1. Table 1. Cartilage thickness and TF soft tissue compressive stiffness estimates S1 (Healthy) Medial (unloaded) Lateral (unloaded) Compressive stiffness 4.7 mm 5.9 mm 2.87*10 6 Nm -1 S2 (ACLD 8 yrs) Medial (unloaded) Lateral (unloaded) Compressive stiffness 6.7 mm 7.3 mm 1.90*10 6 Nm -1 DISCUSSION AND CONCLUSIONS The results indicated distinct alterations in TF proximity during loading. Although cartilage was thicker in the ACLD subject, the stiffness was reduced as anticipated. An associated increase in stiffness was not found. This suggests that cartilage swelling may have been present in S2. Additionally, the decreased stiffness suggests that the load bearing capacity of the joint may be compromised. This combined DF/MRI tool provides a novel methodology for assessing this OA mechanism in-vivo. Future work should evaluate localized changes in bone and cartilage proximities to estimate medial and lateral compartment compressive stiffness following ACL rupture and in OA.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.344
Teacher spread0.290 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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