Cognitive Function in Rheumatoid Arthritis Female Patients: A Prospective Cohort Study
Bibliographic record
Abstract
Aim: To assess the cognitive functions in patients with rheumatoid arthritis (RA) and its correlation with patients’ characteristics. Methods: We conducted a prospective cohort study that included 30 adult females with Rheumatoid Arthritis (RA). We collected the following data from eligible participants: demographic characteristics, disease duration, drug intake, disease activity measured by the disease activity score in 28 joints (DAS28), visual analogue scale (VAS) for global pain assessment, and cognitive function assessment findings.The cognitive assessment was conducted using Montreal cognitive assessment scale (MOCA) and p300 event related potential (ERP). Results: The mean ages of the included patients and control group were 44.97 (±9.58) years and 45.37 (±8.38) years, respectively (p=0.119). In addition, the mean patient global assessment and DAS28 was 5.43 (±2.01) and 5.31(±1.36), respectively. The primary outcome of the present study, showed that the mean MOCA score in patients with RA was 26.43 (±1.92), compared to 28.8 (±0.88) in control group. Similarly, there was a statistically significant difference in p300 ERP between patients with RA and control 413.87(±51.22) versus 278.9 (±29.7) p <0.001). The correlation analysis showed that the p300 ERP values correlated positively with DAS28 (r =0.424, p =0.02), age (r =0.396, p =0.002), and ESR (r =0.482, p =0.007). On the other hand, the MOCA score correlated negatively with patient global assessment scale (r= -0.415, p =0.022). Conclusion: In conclusion, cognitive dysfunction represent another cause of burden in RA. The present study shows that patients with RA had significantly lower cognitive performance and processing than the general population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".