Biomarkers for osteoarthritis: investigation, identification, and prognosis
Bibliographic record
Abstract
Guangju Zhai,1,2 Erfan Aref Eshghi11Faculty of Medicine, Memorial University of Newfoundland, St John's, NL, Canada; 2Department of Twin Research and Genetic Epidemiology, King's College London, London, UKAbstract: Osteoarthritis (OA) is the most common form of arthritis and results in substantial morbidity and disability in the elderly, imposing a great economic burden on society. While there are drugs available on the market that mitigate pain and improve function, there are no disease-modifying osteoarthritis drugs, partly because there is no reliable method that can be used to identify early OA changes. There is a pressing need to develop reliable biomarkers that can inform on the process of joint destruction in OA. Such biomarkers could aid in drug development by identifying fast progressors and detecting early response to therapy, thus reducing patient numbers and time required for clinical trials. Over the last several years, dramatic advances in our understanding of the biochemistry of cartilage have led to a cascade of studies testing proteins as biomarkers of OA. Investigation of single-nucleotide polymorphisms as genetic biomarkers and the application of technologies such as metabolomics to OA are generating potentially additional biomarkers that could help detect early OA changes. This review summarizes the data on the investigation of biochemical and genetic markers in OA and highlights the new biomarkers that are recently reported and their application and limitation in the management of OA. However, despite the dramatic growth of knowledge concerning the discovery of a number of useful biomarkers, the real breakthrough in this area is still not achieved.Keywords: osteoarthritis, biochemical markers, metabolomics, genetics, epigenetics
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".