Tumor Necrosis Factor-α Inhibitor Treatment and the Risk of Incident Cardiovascular Events in Patients with Early Rheumatoid Arthritis: A Nested Case-control Study
Bibliographic record
Abstract
OBJECTIVE: To compare the risk of cardiovascular (CV) events between use of tumor necrosis factor-α inhibitors (TNFi) and nonbiologic disease-modifying antirheumatic drugs (DMARD) in patients with early rheumatoid arthritis (RA). METHODS: A nested case-control study was conducted using data from Truven's MarketScan commercial and Medicare claims database for patients with early RA who started treatment with either a TNFi or a nonbiologic DMARD between January 1, 2008, and December 31, 2010. Date of CV event diagnosis for cases was defined as the event date, and 12 age-matched and sex-matched controls were sampled using incidence density sampling. Drug exposure was defined into the following mutually exclusive categories hierarchically: (1) current use of TNFi (with or without nonbiologics), (2) past use of TNFi (with or without nonbiologics), (3) current use of nonbiologics only, and (4) past use of nonbiologics only. Current use was defined as any use in the period 90 days prior to the event date. Conditional logistic regression models were used to derive incidence rate ratios (IRR). RESULTS: From the cohort of patients with early RA, 279 cases of incident CV events and 3348 matched controls were identified. The adjusted risk of CV events was not significantly different between current TNFi users and current nonbiologic users (IRR 0.92, 95% CI 0.59-1.44). However, past users of nonbiologics showed significantly higher risk compared to current nonbiologic users (IRR 1.47, 95% CI 1.04-2.08). CONCLUSION: No differences in the CV risk were found between current TNFi and current nonbiologic DMARD treatment in patients with early RA.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".