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Record W4411963123 · doi:10.1016/j.ostima.2025.100337

COMPROMISED TRABECULAR BONE OF THE KNEE IS A DOSE-DEPENDENT CORRELATE OF MORE SEVERE OSTEOPHYTES AND ADVANCED KLG

2025· article· en· W4411963123 on OpenAlexaff
Andy Kin On Wong, Sarah Costa, Deepak Jain, María Elena Hernández‐Aguilar, Anna Cagnoni, S. Liu, Vahid Anwari, Ali Naraghi, Rakesh Mohankumar, James D. Johnston, Lora Giangregorio

Bibliographic record

VenueOsteoarthritis Imaging · 2025
Typearticle
Languageen
FieldMedicine
TopicBone health and treatments
Canadian institutionsUniversity of WaterlooUniversity of SaskatchewanPublic Health OntarioArthritis SocietyUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsTrabecular boneMedicineChemistryMaterials scienceDentistryBiomedical engineeringOrthodonticsOsteoporosisInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION Previous studies have shown that bone turnover is elevated, and fracture risk is higher among knee osteoarthritis (KOA) patients, especially in later stages of disease. While there have been mixed findings with respect to areal bone mineral density (BMD)’s association with KOA severity, it remains unclear how volumetric bone morphometry at the knee is related to the development of radiographic disease features such as osteophytosis and attrition. OBJECTIVE It was hypothesized that having definite osteophytosis and attrition are each associated with compromised subchondral bone, including lower volumetric(v) BMD, apparent v.Tissue Mineral Density (vTMD) and a wider Tb.Sp. METHODS In this cross-sectional study, women 50-85 years old were recruited by convenience sample if they experienced knee pain ≥3 days a week, each lasting >3 hours, and if self-reported body mass index (BMI) was <30 kg/m 2 . On the knee with worse symptoms, they completed a peripheral quantitative CT (pQCT) knee scan, one slice (2.3±0.5mm, 200µm in-plane) prescribed per tibiofemoral compartment; and an anteroposterior knee X-ray for KLG, including breakdown semi-quantitative evaluation of osteophytosis, attrition, JSN, and sclerosis. pQCT knee images were analyzed using a previously reported iterative threshold-seeking algorithm (Tam et al. Skeletal Muscle 27(14) 2024) to separate trabecular bone from marrow. Apparent structural parameters were derived from bone volume, bone surface, and total volume according to equations by Parfitt’s model of parallel plates. General linear models examined how KLG and osteophyte score, and each of established (score > 2) KOA (KL), osteophytosis, and attrition were related to knee vBMD, vTMD, app: Tb.Sp, Tb.Th, Tb.N, and BV/TV. Models adjusted for age, BMI, use of pain medications, antiresorptives, glucocorticoids or intra-articular steroid injections. RESULTS Among 105 women (mean(SD) age: 62.6(9.0)yrs, BMI: 24.2(3.5)kg/m 2 , median KLG: 1(1,2), 41(39.1%) with established KOA), a higher KLG or established KOA were each associated with lower vBMD and vTMD (with effects larger for vTMD), and a larger app.Tb.Sp; though, only in advanced stage (KLG3/4) individuals (Table1). Attrition was only associated with larger Tb.Sp in the lateral femur. Having more advanced osteophytosis was dose-dependently linked to lower vBMD and larger app.Tb.Sp (Figure 1). These effects were only present at the femur and not the tibia, with magnitudes appearing larger in themedial compartment among moderate grade (score 2) knees, but dose-dependently only in the lateral compartment. CONCLUSION Among peri- to post-menopausal women without obesity, compromised bone characterized by lower apparent bone density and less intact trabecular structure, may be key correlates of having more advanced radiographic KOA largely driven by osteophytosis. Structural differences may not be adequately apparent in the subchondral tibia using pQCT, perhaps due to damage that may simulate higher bone volume fraction. More sensitive techniques or metrics are needed to distinguish damaged bone from intact but diminished structures.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.005
GPT teacher head0.250
Teacher spread0.245 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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