Decreased flow-mediated dilatation in patients with rheumatoid arthritis: a meta-analysis
Bibliographic record
Abstract
ABSTRACT Objectives To derive a more precise comparison of flow-mediated dilatation (FMD%) of the brachial artery between patients with rheumatoid arthritis (RA) and normal controls by performing a meta-analysis of appropriate studies. Methods PubMed and EMBASE databases were searched for all relevant articles. STATA (V.12.0) software was used to perform the meta-analysis. Quality estimation of all appropriate studies was evaluated according to the Newcastle-Ottawa Scale (NOS). Standardised mean difference (SMD) with 95% CIs were calculated with a random-effects model. The Cochrane Q test and I2 statistic were used to evaluate the heterogeneity. Funnel plot and Egger’s test were conducted to assess the publication bias. Results In total, 464 articles were obtained after searching the two databases. Ten studies were included in the meta-analysis on the basis of the inclusion and exclusion criteria. Significant heterogeneity was observed among these 10 studies (Q=102.89, p<0.001, I2=91.3%) with random-effects modelling. The results showed that the RA group had significantly lower FMD% (SMD: −1.405; 95% CI −1.992 to −0.817; p<0.001) than the control group. Egger’s test (p=0.004) indicated that the funnel plot showed a skewed or asymmetrical shape and publication bias existed. Sensitivity analyses suggested the robustness and credibility of our results. Conclusions FMD% in patients with RA is significantly decreased compared with healthy controls. FMD% is an important early marker of atherosclerosis. It may be used as a parameter to forecast cardiovascular disease in patients with RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.062 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".