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Record W2592090300 · doi:10.3899/jrheum.160226

The Effect of Aging and Mechanical Loading on the Metabolism of Articular Cartilage

2017· review· en· W2592090300 on OpenAlexvenueno aff
Adam El Mongy Jørgensen, Michael Kjær, Katja M. Heinemeier

Bibliographic record

VenueThe Journal of Rheumatology · 2017
Typereview
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsnot available
FundersNovo Nordisk FondenLundbeckfonden
KeywordsMedicineOsteoarthritisCartilageSenescenceProteoglycanInflammationMuscle hypertrophyDegeneration (medical)Articular cartilageDecorinAtrophyChondrocyteCatabolismCell biologyInternal medicineAnatomyPathologyMetabolismBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: The morphology of articular cartilage (AC) enables painless movement. Aging and mechanical loading are believed to influence development of osteoarthritis (OA), yet the connection remains unclear. METHODS: This narrative review describes the current knowledge regarding this area, with the literature search made on PubMed using appropriate keywords regarding AC, age, and mechanical loading. RESULTS: Following skeletal maturation, chondrocyte numbers decline while increasing senescence occurs. Lower cartilage turnover causes diminished maintenance capacity, which produces accumulation of fibrillar crosslinks by advanced glycation end products, resulting in increased stiffness and thereby destruction susceptibility. CONCLUSION: Mechanical loading changes proteoglycan content. Moderate mechanical loading causes hypertrophy and reduced mechanical loading causes atrophy. Overloading produces collagen network damage and proteoglycan loss, leading to irreversible cartilage destruction because of lack of regenerative capacity. Catabolic pathways involve inflammation and the transcription factor nuclear factor-κB. Thus, age seems to be a predisposing factor for OA, with mechanical overload being the likely triggering cause.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.980
Threshold uncertainty score0.334

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
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 head0.032
GPT teacher head0.325
Teacher spread0.293 · 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 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

Citations151
Published2017
Admission routes1
Has abstractyes

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