Rheumatoid Arthritis Disease Activity and Disability Affect the Risk of Serious Infection Events in RADIUS 1
Bibliographic record
Abstract
OBJECTIVE: To determine whether disease activity and disability independently correlate with serious infection event (SIE) risk in a large rheumatoid arthritis (RA) cohort. METHODS: The associations between SIE and Clinical Disease Activity Index (CDAI) and Health Assessment Questionnaire-Disability Index (HAQ-DI) in the Rheumatoid Arthritis Disease-Modifying Antirheumatic Drug Intervention and Utilization Study (RADIUS 1) cohort were evaluated using the Andersen-Gill model (a proportional HR model allowing > 1 event per patient). RESULTS: Of 4084 patients with 347 SIE, 271 patients experienced ≥ 1 SIE. A 5-unit CDAI increase and 0.4-unit HAQ-DI increase corresponded to an increase in SIE risk with and without covariate adjustments. A 5-unit CDAI increase corresponded with a 7.7% increased SIE risk (adjusted HR 1.077, 95% CI 1.044-1.112, p < 0.0001) and a 0.4-unit HAQ-DI increase with a 30.1% increased risk (adjusted HR 1.301, 95% CI 1.225-1.381, p < 0.0001). Categorical analysis showed that more severe RA activity (even after controlling for disability) and disability were associated with an increased SIE risk. CONCLUSION: Increased RA disease activity and disability were each associated with a significantly increased SIE risk in the RADIUS 1 cohort, which could not be completely accounted for by disability.
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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 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 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".