Biologics and the Cardiovascular System: A Double-Edged Sword
Bibliographic record
Abstract
Patients with chronic inflammatory diseases such as rheumatoid arthritis have a higher risk of cardiovascular diseases and related mortality compared to the general population. This risk is first due to classical cardiovascular risk factors but also due to systemic inflammation which is independently involved, causing accelerated atherosclerosis, myocardial infarction, cerebrovascular disease and heart failure (HF). Pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-alpha), interleukin (IL)-1 and IL-6 could be major actors on this pathophysiology. Biologics are effective specific treatments in the management of inflammatory rheumatic and systemic diseases. In this review, beneficial and deleterious effects on the heart and vessels of the biologics used in the management of inflammatory arthritis and vasculitides will be discussed, focusing on TNF-alpha, IL-6 and IL-1 blockades, and anti-CD20. Noninflammatory cardiac conditions, such as heart failure, myocardial infarction, and cardiovascular conditions such as atherosclerosis, as well as inflammatory diseases including vasculitides will be discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".