Anti-rheumatic drug use and risk of hospitalization for congestive heart failure in rheumatoid arthritis
Bibliographic record
Abstract
OBJECTIVE: To assess the risk of hospitalization for congestive heart failure (CHF) associated with the use of disease-modifying anti-rheumatic drugs (DMARDs) and other medications used in RA. METHODS: We used a case-control design nested within an administrative database cohort of patients with rheumatoid arthritis (RA) who were dispensed a DMARD between September 1998 and December 2001. Subjects identified with a prior history of CHF were excluded. For each hospitalized case of CHF identified during follow-up, 10 controls matched on age and time were randomly selected from the cohort. Conditional logistic regression was used to estimate the rate ratio (RR) of hospitalizations for CHF associated with the current use of specific drugs, adjusted for sex and co-morbidity. RESULTS: The cohort included 41 885 patients; 75% were women, with an average age at cohort entry of 51 yr. During follow-up, 520 hospitalizations for CHF occurred, for a rate of 10.1 per 1000 per year. The adjusted RR of CHF for current use of any DMARD was 0.7 (95% CI 0.6-0.9) relative to no current use. By DMARD category, there was evidence of a beneficial effect for both tumour necrosis factor-alpha antagonists (RR 0.5, 95% CI 0.2-0.9) and methotrexate monotherapy (RR 0.8, 95% CI 0.6-1.0). For non-DMARD medications, the rate of CHF was not clearly increased or decreased, except for COX-2 inhibitors. The data suggested an increased risk of CHF with rofecoxib (RR 1.3, 95% CI 1.0-3.1) and a decreased risk of CHF with celecoxib (RR 0.6, 95% CI 0.4, 1.0). CONCLUSIONS: The use of DMARDs was associated with a reduction in the risk of hospitalizations for CHF in this RA cohort. The increased risk with rofecoxib alongside a decreased risk with celecoxib suggests the absence of a class effect with respect to COX-II inhibitors for some types of cardiovascular morbidity.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".