Screening of Cognitive Impairment in patients with Rheumatoid arthritis
Bibliographic record
Abstract
Abstract Background Rheumatoid arthritis (RA) is a chronic inflammatory disease that affects 0.5‐1% of the population, mainly women and older adults. There is increasing evidence of the presence of cognitive impairment in patients with RA, with a prevalence of around 30%. Some underlying mechanisms involved could be inflammation within the brain, high cardiovascular comorbidity, and the use of immunosuppressive agents that can be neurotoxic with prolonged use, among others. The Montreal Cognitive Assessment (MoCA) is a validated tool for detecting cognitive impairment in our country. The objective of this study is to determine the frequency of cognitive impairment in patients with RA compared to a group of patients with osteoarthritis. Method We assessed adult patients diagnosed with RA (2010 ACR/EULAR criteria) and osteoarthritis (ACR criteria) according to selection criteria. Demographic and clinical data were collected. The MoCA test was administered to all patients considering the cut‐off value <26 points to define the presence of cognitive impairment. The study was approved by the institutional ethics and research committee. Result A total of 60 patients were included: 40 with RA (G1) and 20 with osteoarthritis (G2), the majority being women in both groups, mean age was 66.9 (SD 11.7) years and 71.4 (SD 10.6) years, respectively. There were no statistically significant differences in education, socioeconomic status, or vascular risk factors among groups. The frequency of cognitive impairment was significantly higher in G1 compared to G2: 42.5% (95% CI 28‐58.5) versus 15% (95% CI 4‐40)(p 0.026). Regarding MoCA domains, attention and memory were more affected in G1 compared to G2. In the multivariate analysis, the diagnosis of RA and older age were factors significantly associated with cognitive impairment. Conclusion In our RA population, the frequency of cognitive impairment was 42.5%. RA diagnosis and older age were factors related to cognitive impairment. A substantial number of patients and the administration of a standard neuropsychological test will help us to validate these results.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".