Systematic review and meta‐analysis: Anti–tumor necrosis factor α therapy and cardiovascular events in rheumatoid arthritis
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Control of rheumatoid arthritis (RA) may reduce the risk of cardiovascular events. We sought to systematically assess the association between anti-tumor necrosis factor α (anti-TNFα) therapy in RA and cardiovascular event rates. METHODS: Observational cohorts and randomized controlled trials (RCTs) reporting on cardiovascular events (all events, myocardial infarction [MI], congestive heart failure, and cerebrovascular accident [CVA]) in RA patients treated with anti-TNFα therapy compared to traditional disease-modifying antirheumatic drugs were identified from a search of PubMed (1950 to November 2009), EMBase (1980 to November 2009), and conference abstracts. Relative risks (RRs) or hazard ratios and 95% confidence intervals (95% CIs) were extracted. If the incidence was reported, additional data were extracted to calculate an incidence density ratio and its variance. RESULTS: The systematic review and meta-analysis include 16 and 11 publications, respectively. In cohort studies, anti-TNFα therapy was associated with a reduced risk for all cardiovascular events (pooled adjusted RR 0.46; 95% CI 0.28, 0.77), MI (pooled adjusted RR 0.81; 95% CI 0.68, 0.96), and CVA (pooled adjusted RR 0.69; 95% CI 0.53, 0.89). Meta-analysis of RCTs also produced a point estimate indicating lower risk of cardiovascular events, but this was not statistically significant (pooled RR 0.85; 95% CI 0.28, 2.59). CONCLUSION: Anti-TNFα therapy is associated with a reduced risk of all cardiovascular events, MI, and CVA in observational cohorts. There was heterogeneity among cohort studies and possible publication bias. The point estimate of the effect from RCTs is underpowered with wide 95% CIs, and cardiovascular events were secondary outcomes, but RCTs also demonstrated a trend toward decreased risk.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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