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Record W3138578887 · doi:10.1002/acr.24604

Magnetic Resonance Imaging–Defined Osteoarthritis Features and Anterior Knee Pain in Individuals With, or at Risk for, Knee Osteoarthritis: A Multicenter Study on Osteoarthritis

2021· article· en· W3138578887 on OpenAlexfundno aff
Erin M. Macri, Tuhina Neogi, Mohamed Jarraya, Ali Guermazi, Frank W. Roemer, Cora E. Lewis, James C. Torner, J.A. Lynch, Irina Tolstykh, S. Reza Jafarzadeh, Joshua J. Stefanik

Bibliographic record

VenueArthritis Care & Research · 2021
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesNational Institute of General Medical SciencesCanadian Institutes of Health ResearchNational Institute on AgingNational Institutes of Health
KeywordsOsteoarthritisMedicineMagnetic resonance imagingKnee painOdds ratioInfrapatellar fat padConfidence intervalSynovitisKnee JointJoint effusionPatellaAnterior knee painPhysical therapyInternal medicineArthritisRadiologyOrthodonticsSurgeryPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: The lack of strong association between knee osteoarthritis (OA) structural features and pain continues to perplex researchers and clinicians. Evaluating the patellofemoral joint in addition to the tibiofemoral joint alone has contributed to explaining this structure-pain discordance, hence justifying a more comprehensive evaluation of whole-knee OA and pain. The present study, therefore, was undertaken to evaluate the association between patellofemoral and tibiofemoral OA features with localized anterior knee pain (AKP) using 2 study designs. METHODS: Using cross-sectional data from the Multicenter Osteoarthritis Study, our first approach was a within-person, knee-matched design in which we identified participants with unilateral AKP. We then assessed magnetic resonance imaging (MRI)-derived OA features (cartilage damage, bone marrow lesions [BMLs], osteophytes, and inflammation) in both knees and evaluated the association of patellofemoral and tibiofemoral OA features to unilateral AKP. In our second approach, MRIs from 1 knee per person were scored, and we evaluated the association of OA features to AKP in participants with AKP and participants with no frequent knee pain. RESULTS: Using the first approach (n = 71, 66% women, mean ± SD age 69 ± 8 years), lateral patellofemoral osteophytes (odds ratio [OR] 5.0 [95% confidence interval (95% CI) 1.7-14.6]), whole-knee joint effusion-synovitis (OR 4.7 [95% CI 1.3-16.2]), and infrapatellar synovitis (OR 2.8 [95% CI 1.0-7.8]) were associated with AKP. Using the second approach (n = 882, 59% women, mean ± SD age 69 ± 7 years), lateral and medial patellofemoral cartilage damage (prevalence ratio [PR] 2.3 [95% CI 1.3-4.0] and PR 1.9 [95% CI 1.1-3.3], respectively) and lateral patellofemoral BMLs (PR 2.6 [95% CI 1.5-4.7]) were associated with AKP. CONCLUSION: Patellofemoral but not tibiofemoral joint OA features and inflammation were associated with AKP.

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)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.298
Teacher spread0.282 · 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 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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Citations24
Published2021
Admission routes1
Has abstractyes

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