Systematic review and meta-analysis: dipeptidyl peptidase-4 inhibitors and rheumatoid arthritis risk
Bibliographic record
Abstract
This review evaluated the risk of rheumatoid arthritis in patients with type 2 diabetes treated with dipeptidyl peptidase-4 inhibitors (Dpp-4i). The MEDLINE (via PubMed), Embase, the Cochrane Library databases and web of science were used to search the effects of Dpp-4i on rheumatoid arthritis in patients with type 2 diabetes from inception to 7 September, 2020. We included studies that met the following criteria:(i) A randomized controlled trial (RCT), prospective or retrospective cohort study examining the relationship between Dpp-4i and rheumatoid arthritis. Exclusion criteria included the following: Reviews and researches related to other diseases or subjects; and studies without data on the prevalence of rheumatoid arthritis were excluded. Risk of Bias table contained in Review Manager 5.3 and Newcastle-Ottawa scale (NOS) were used for quality assessment of included RCT and observational studies separately. Meta-analysis was used to estimate the risk of disease. We conducted a subgroup analysis of duration of follow-up, adjusted (adjusted RR or unadjusted RR), sample size and study design. A total of 10 independent studies assessing 1,420,414 patients were included in this analysis. In this meta-analysis, we found that there was nonsignificant increase of rheumatoid arthritis with Dpp-4 inhibitor exposure (RR 0.96, 95%CI (0.69-1.32)). Our results revealed that Dpp-4 inhibitors do not seem to increase the risk of rheumatoid arthritis. Long-term follow-up monitoring is necessary.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.037 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".