Anti-rheumatic drug use and risk of serious infections in rheumatoid arthritis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To assess the risk of severe infections associated with the use of traditional disease-modifying anti-rheumatic drugs (DMARDs) and glucocorticoid agents in rheumatoid arthritis (RA). METHODS: Our study was a case-control design nested within a cohort of 23 733 RA patients studied between 1 January 1980 and 31 December 2003. Matching on age and gender, and adjusting for comorbidity and physician use, conditional logistic regression was used to estimate the effect of specific drugs on the rate ratio (RR) for infections requiring hospitalization. RESULTS: The risk for all infections requiring hospitalization appeared to be most elevated with current exposures to cyclophosphamide [RR: 3.26, 95% confidence interval (CI): 2.28-4.67] and systemic glucocorticoid agents (RR: 2.56, 95% CI: 2.29-2.85); azathioprine was associated with a moderate increased risk (RR: 1.52, 95% CI: 1.18-1.97). There was a suggestion of increased risk of pneumonia due to methotrexate (RR: 1.16, 95% CI: 1.02-1.33). The results were similar for the period before and after the introduction of anti-tumour necrosis factor (TNF) agents. The RR point estimate for anti-TNF agents suggested about a 2-fold increased risk for all infections, but the estimate was imprecise. CONCLUSIONS: In this large cohort of RA patients, the most heightened risk of serious infections was seen with the use of glucocorticoid agents and immunosuppressive DMARDs. Assessments of infection risk related to newer and emerging therapies should carefully consider concomitant medication exposures, including traditional DMARDs and glucocorticoid therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it