Neopterin serum level does not reflect the disease activity in rheumatoid arthritis: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Rheumatoid arthritis (RA) is a chronic autoimmune disease caused by established chronic inflammation. Neopterin levels have extensively been considered as a marker of immune activation during inflammation. In this study, we performed a systematic evaluation and meta‐analysis to elucidate the overall relationship between neopterin concentration and RA disease activity. Following the Preferred Reporting Items for Systematic Reviews and Meta‐Analysis guidelines, a systematic review was conducted using PubMed, Google Scholar, Web of Science, and Scopus from 2000 to August 2020. The Newcastle–Ottawa scale was used to assess the quality of eligible studies. The effect size (ES) and corresponding 95% confidence intervals (CIs) were calculated to evaluate this association. A total of 15 studies out of 98 met our inclusion criteria. The pooled analysis found that patients with RA had high level of neopterin; however, no statistically significant association was found between neopterin levels with high, intermediate, and low diseases activity score (DAS)‐28 (ES =11.18, 95% CI: 6.02 to 16.34, and I2 = 91.8%; and ES = 8.57, 95% CI: 6.41 to 10.37, and I2 = 99.5%; and ES =12.45, 95% CI: −1.68 to 26.58, and I2 = 99.0%, respectively). Our results indicated that the neopterin concentration does not seem to have any substantial impact on the RA disease activity.
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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 | 0.027 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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".