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Record W2020190409 · doi:10.1097/bor.0b013e328307f58c

Abnormal and cumulative loading in knee osteoarthritis

2008· review· en· W2020190409 on OpenAlexaff
Monica R. Maly

Bibliographic record

VenueCurrent Opinion in Rheumatology · 2008
Typereview
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOsteoarthritisMedicineBiomechanicsPhysical medicine and rehabilitationKnee JointGaitKinematicsPhysical therapySurgeryAnatomyPathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review examines recent advances in understanding the abnormal mechanics characteristic of knee osteoarthritis and provides a rationale to assess the total exposure to knee loading during daily activity. RECENT FINDINGS: The abnormal loading environment in knee osteoarthritis is represented by the knee adduction moment. While knee osteoarthritis gait research focuses on this variable, emerging evidence supports a critical role for knee kinematics, muscle activation patterns and the kinematics and kinetics of other lower extremity joints in the development and progression of this disease. Nevertheless, abnormal knee loading is not the only cause of articular cartilage disruption. Excessive and repetitive loading, together creating a total exposure to loading, are critical factors in knee pathomechanics. To assess excessive and repetitive loading, cumulative load is a biomechanical approach that integrates loading exposures to represent the accumulated load that knee tissues endure during physical activity. SUMMARY: Knee osteoarthritis pathomechanics involves an interaction between abnormal and excessive and/or repetitive loading. Mechanics of lower extremity joints and muscle activation patterns influence the knee loading environment. Future work could integrate measures of abnormal loading with assessments of the total exposure to loading during physical activity to better link biomechanics with clinical outcomes in knee osteoarthritis.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
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 head0.071
GPT teacher head0.369
Teacher spread0.298 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations102
Published2008
Admission routes1
Has abstractyes

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