Bibliographic record
Abstract
BACKGROUND: Investigators have explored the association between diabetes mellitus and arthritis for a long time; however, there are uncertainties and inconsistencies among various studies. In this study, we tried to explore the relationship between diabetes mellitus and the overall risk of arthritis, as well as the potential modifiers for this relationship. METHODS: We conducted a comprehensive literature search through PubMed and identified 36 eligible studies. The overall analyses, subgroup analyses, as well as sensitivity analyses, were conducted to illustrate the association between diabetes mellitus and arthritis. Study quality was evaluated using the Newcastle-Ottawa Quality Assessment Scale. All statistical analyses were conducted using STATA SE version 13.0. RESULTS: In our study, 36 eligible studies were identified and involved in the meta-analysis. The overall association between diabetes mellitus and arthritis is 1.61 (95% confidence interval [CI]: 1.14-2.28, P = .007). The association exists only in nongouty arthritis, where we observed the estimated odds ratio (OR) 1.33 (95% CI: 1.05-1.67, P < .001). The opposite point estimates from different types of diabetes may indicate possible different associations for type I (OR: 0.98, 95% CI: 0.18-5.39, P = .985) or type II diabetes (OR: 1.28, 95% CI: 0.88-1.84, P = .194). CONCLUSION: Diabetes mellitus performs more likely as a comorbidity of arthritis rather than a risk factor; however, more studies will be helpful to increase the confidence of identifying the association between diabetes and arthritis.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".