Mild cognitive impairment and rheumatoid arthritis
Bibliographic record
Abstract
Abstract Background Increasing evidence from the last ten years suggests that systemic inflammation may be associated with an increased risk of developing Alzheimer’s disease (AD). Some studies have found an association between rheumatoid arthritis (RA) and neurodegeneration with several finding increased incidence of mild cognitive impairment (MCI) in those with RA compared to healthy controls. This study aims to use preliminary data from the Rheumatoid arthritis, medication and memory study (RESIST) to investigate the prevalence of MCI in a population of patients with RA and explore the relationship between MCI and specific demographic and clinical characteristics. Method The Montreal Cognitive Assessment (MoCA) was used as a cognitive screening tool and was administered to subjects who were ≥55 years of age and had been diagnosed with RA according to the American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) criteria. Demographic and clinical data was recorded at screening in face‐to‐face interviews and included age, gender, type of RA medication and date of RA diagnosis. 260 participants completed both screening and baseline visits as part of the RESIST longitudinal study, MoCA scores from baseline were analysed for these participants. Statistical analysis was used to provide descriptive statistics for our population and determine predictors of cognitive impairment. Result A total of 636 participants (mean age 68.1, 67.5% female) were screened between May 2018 and December 2019. The mean MoCA for screened participants was 25.4, 45.3% of our population scored <26 in the MoCA and were considered cognitively impaired. Age was negatively correlated to MoCA score and was the only significant predictor of cognitive impairment (p<0.001). Gender, type of RA medication and duration of disease did not contribute to the regression model. The mean MoCA score was found to differ by 0.404 points between screening and baseline. Conclusion A large proportion of our participants scored below the proposed cut‐off for normal cognition in the MoCA suggesting at the high prevalence of MCI in older adults with RA. This may provide further support for the role of chronic inflammation in AD and questions whether it would be worthwhile introducing routine screening for MCI in rheumatology clinics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 0.001 |
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".