Osteoarthritis year in review 2023: metabolite and protein biomarkers
Bibliographic record
Abstract
OBJECTIVE: To highlight the advances over the past year in metabolite/protein biomarkers for osteoarthritis (OA). METHOD: A literature search of five databases including PubMed, Web of Science, Scopus, Ovid Medline, and Embase was performed for studies on metabolite/protein/peptide/biochemical markers for OA published between April 1st, 2022 and March 31st, 2023. Records were then screened to include only original research articles using directly collected human specimens, in English language, and with full text available. Data from eligible studies were systematically extracted and summarized. RESULTS: A total of 1600 unique records were extracted, out of which 46 fulfilled the inclusion criteria and were used for data extraction. Forty-one of these 46 studies focused on biomarkers for OA/OA severity/progression, four on OA clustering, and one on OA treatment outcomes. Twenty-nine studied protein markers for OA, thirteen studied metabolite markers, and four studied both. While many studies were the validation of the previously reported biomarkers, a number of novel metabolite/protein biomarkers and biomarker panels were reported in the past year. Biomarker panels might be useful to subset OA patients. CONCLUSION: The number of studies on OA clustering is rising. Although validation in larger cohorts is needed in order to utilize reported biomarkers in clinical practice, these discoveries help better understand the pathogenesis of OA, provide insights into possible mechanisms underlying poor treatment outcomes, and aid in developing personalized treatment based on OA subtypes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".