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Record W3159324680 · doi:10.1016/j.ocarto.2021.100170

The role of metabolomics in precision medicine of osteoarthritis: How far are we?

2021· article· en· W3159324680 on OpenAlex
Guangju Zhai

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

VenueOsteoarthritis and Cartilage Open · 2021
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsMemorial University of Newfoundland
FundersCanadian Institutes of Health ResearchMemorial University of Newfoundland
KeywordsMetabolomicsHypotaurineMedicineTaurineOsteoarthritisPopulationNarrative reviewBioinformaticsOmicsPathologyBiologyIntensive care medicineAlternative medicineBiochemistry

Abstract

fetched live from OpenAlex

Objectives: A narrative review on recent published studies of metabolomics in osteoarthritis (OA) with the focus on how the metabolomic findings help stratify OA patients in precision medicine. Design: A narrative review based on selected population-based metabolomics studies in OA. Results: studies, animal models. Thirty-two population-based metabolomic studies using either plasma/serum, synovial fluid, cartilage, or subchondral bone samples were reviewed. The most reported metabolic pathways to be involved in OA included energy metabolic pathways, arginine and proline metabolism, taurine and hypotaurine metabolism, and glycerophospholipid metabolism. Conclusions: While metabolomics of OA research is still in its infancy, the published data showed that metabolomics is a promising tool to help better understanding of pathogenesis of OA, classify OA patients into different endotypes, and develop precision medicine tools for OA management.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.015
GPT teacher head0.257
Teacher spread0.242 · 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