Systematic review and meta‐analysis: Anti–tumor necrosis factor α therapy and cardiovascular events in rheumatoid arthritis
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.
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.
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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".