Association between hyperuricemia, gout, urate lowering therapy, and osteoarthritis
Bibliographic record
Abstract
INTRODUCTION: Osteoarthritis (OA), a chronic and degenerative joint disease characterized by articular cartilage degeneration, sclerosis of subchondral bone, and osteophyte formation, is deemed a leading cause of activity limitation and disability among the elderly people. Serum uric acid (UA) is a terminal metabolite of purine compound, while hyperuricemia (HU) and UA crystals are recognized causes of gout. Several studies have investigated the correlations between HU, gout and OA, but the findings are inconclusive. We are also concerned whether the urate lowering therapy (ULT) can become a potential treatment for OA and intend to undertake this meta-analysis to clarify the related hypotheses. METHODS: Systematic literature search will be conducted on PubMed, Embase, and Web of Science to identify relevant studies up to February 2020 using appropriate search strategies. All citations and abstracts retrieved from literature search will be assessed by two reviewers independently. The Newcastle-Ottawa Scale or the Cochrane risk of bias assessment tool will be used as appropriate to assess the quality and the risk of bias of the included studies. The heterogeneity and the publication bias of the studies will be investigated accordingly. RESULTS: We propose to undertake this meta-analysis as a feasible approach to clarify the associations between HU, gout or ULT, and OA. DISCUSSIONS: This meta-analysis will help to strengthen our knowledge of the pathogenesis of OA and promote the development of preventive or treatment strategies. REGISTRATION: PROSPERO registration number CRD42020168769.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".