Osteoarthritis year in review: genetics, genomics, epigenetics
Bibliographic record
Abstract
OBJECTIVE: In this review, we have highlighted advances in genetics, genomics and epigenetics in the field of osteoarthritis (OA) over the past year. METHODS: A literature search was performed using PubMed and the criteria: "osteoarthritis" and one of the following terms "genetic(s), genomic(s), epigenetic(s), epigenomic(s), noncoding RNA, microRNA, long noncoding RNA, lncRNA, circular RNA, RNA sequencing, single cell sequencing, or DNA methylation between April 1, 2019 and April 30, 2020. RESULTS: We identified 653 unique publications, many studies spanned multiple search terms. We summarized advances relating to evolutionary genetics, pain, ethnicity specific risk factors, functional studies of gene variants, and interactions between coding and non-coding RNAs in OA pathogenesis. CONCLUSIONS: Studies have identified variants contributing to OA susceptibility, candidate biomarkers for diagnosis and prognosis, as well as promising therapeutic candidates. Validation in multiple cohorts, multi-omics strategies, and machine learning aided computational analyses have all contributed to the strength of published literature. Open access data-sets, greater sample sizes to capture broader populations and understanding disease mechanisms by investigating the interactions between multiple tissue types will further aid in progress towards understanding and curing OA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".