The Use of Anti-Platelet and/or Anticoagulant Agents in the Prevention of Large Vessel Vasculitis-Associated Ischemic Complications: A Meta-Analysis
Bibliographic record
Abstract
Objective: To determine the effectiveness of antiplatelet and/or anticoagulant therapy (AP/AC) at reducing ischemic events in patients with Large Vessel Vasculitis (LVV). Methods: We performed a random effects meta-analysis of studies examining antiplatelet and/or anticoagulant therapy (AP/AC) and ischemic events in Takayasu’s Arteritis (TAK) or Giant Cell Arteritis (GCA). Severe ischemic events were defined as stroke, ischemic ocular manifestations and claudication symptoms. Any ischemic event included jaw claudication in addition to the above manifestations. Results: Seven studies met inclusion criteria: 1 TAK and 6 GCA. The majority of patients (>80%) were treated with ASA and treatment was initiated prior to diagnosis of LVV. Risk of severe and any ischemic event in patients with LVV treated with AP/AC versus no treatment was not significantly different (OR 0.570, 95% CI 0.243, 1.340 and OR 0.594, 95% CI 0.248, 1.421, respectively). For studies with follow-up data (26-76 months), AP/AC was protective for severe ischemic events (OR 0.18, 95% CI 0.04, 0.83). Findings were similar when excluding studies that did not account for potential confounders, such as cardiovascular risk factors. Conclusion: At follow-up, antiplatelet therapy significantly decreases ischemic events in patients with LVV. However, in most cases of GCA, the treatment was initiated prior to the diagnosis of vasculitis. The benefit of initiating anti-platelet therapy at the time of GCA diagnosis remains unclear.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.052 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".