Similarity of Response to Biologics Between Elderly-onset Rheumatoid Arthritis (EORA) and Non-EORA Elderly Patients: From the FIRST Registry
Bibliographic record
Abstract
OBJECTIVE: Increasing numbers of patients are developing rheumatoid arthritis (RA) at an older age, and optimal treatment of patients with elderly-onset RA (EORA) is attracting greater attention. This study aimed to analyze the efficacy and safety of biologic/targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) in EORA and non-EORA elderly patients. METHODS: A cohort of patients with RA treated with b/tsDMARDs were retrospectively analyzed. Only patients aged ≥ 60 years were included. Among them, patients who developed RA aged ≥ 60 years were categorized as EORA, whereas those aged < 60 years were categorized as non-EORA elderly. Disease activity was compared between the EORA and non-EORA elderly groups. RESULTS: In total, 1040 patients were categorized as EORA and 710 as non-EORA elderly. There were no significant differences in characteristics at baseline between the 2 groups. The proportion of patients with low and high disease activity was comparable at Weeks 2, 22, and 54 between the EORA and the non-EORA elderly group. There were no significant differences in the reasons for the discontinuation of b/tsDMARDs between the 2 groups. Elderly RA onset did not affect changes in Clinical Disease Activity Index (CDAI) and Health Assessment Questionnaire-Disability Index, nor did it affect the reasons for b/tsDMARD discontinuation between the 2 groups. The trajectory analysis on CDAI responses to b/tsDMARDs for 54 weeks identified 3 response patterns. The proportion of patients categorized into each group and CDAI response trajectories to b/tsDMARDs were very similar between EORA and non-EORA elderly patients. CONCLUSION: CDAI response patterns to b/tsDMARDs and HR of adverse events were similar between EORA and non-EORA elderly patients.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".