The RESIST Study: Examining Cognitive Change in Rheumatoid Arthritis Patients with Mild Cognitive Impairment Being Treated with a TNF-Inhibitor Compared to a Conventional Synthetic Disease-Modifying Anti-Rheumatic Drug
Bibliographic record
Abstract
Background: Evidence suggests that TNF inhibitors (TNFi) used to treat rheumatoid arthritis (RA) may protect against Alzheimer's disease progression by reducing inflammation. Objective: To investigate whether RA patients with mild cognitive impairment (MCI) being treated with a TNFi show slower cognitive decline than those being treated with a conventional synthetic disease-modifying anti-rheumatic drug (csDMARD). Methods: 251 participants with RA and MCI taking either a csDMARD (N = 157) or a TNFi (N = 94) completed cognitive assessments at baseline and 6-month intervals for 18 months. It was hypothesized that those taking TNFis would show less decline on the primary outcome of Free and Cued Selective Reminding Test with Immediate Recall (FCSRT-IR) and the secondary outcome of Montreal Cognitive Assessment (MoCA). Results: No significant changes in FCSRT-IR scores were observed in either treatment group. There was no significant difference in FCSRT-IR between treatment groups at 18 months after adjusting for baseline (mean difference = 0.5, 95% CI = -1.3, 2.3). There was also no difference in MoCA score (mean difference = 0.4, 95% CI = -0.4, 1.3). Conclusions: There was no cognitive decline in participants with MCI being treated with TNFis and csDMARDs, raising the possibility both classes of drug may be protective. Future studies should consider whether controlling inflammatory diseases using any approach is more important than a specific therapeutic intervention.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".