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Record W3048625035 · doi:10.3389/fmed.2020.00376

MicroRNAs in Synovial Pathology Associated With Osteoarthritis

2020· review· en· W3048625035 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFrontiers in Medicine · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersCanadian Arthritis NetworkNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsArthritis Society
KeywordsOsteoarthritismicroRNAMedicineDiseaseArthritisSynovial membraneInflammationSynovial jointBioinformaticsPathologyImmunologyBiologyGeneArticular cartilageGenetics

Abstract

fetched live from OpenAlex

Osteoarthritis (OA) is the most common type of arthritis, a disease that affects the entire joint. The relative involvement of each tissue, and their interactions, add to the complexity of OA, hampering our understanding of the underlying molecular mechanisms, and the generation of a disease modifying therapy. The synovium is essential in maintaining joint homeostasis, and pathologies associated with the synovium contribute to joint destruction, pain and stiffness in OA. MicroRNAs (miRNAs) are post-transcriptional regulators dysregulated in OA tissues including the synovium. MiRNAs are important contributors to OA synovial changes that have the potential to improve our understanding of OA and to act as novel therapeutic targets. The purpose of this review is to summarize and integrate current published literature investigating the roles that miRNAs play in OA-related synovial pathologies including inflammation, matrix deposition and cell proliferation.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.266
Teacher spread0.254 · 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