Arterial Thrombosis in Patients with Antiphospholipid Syndrome: A Review and Meta-Analysis
Bibliographic record
Abstract
There is a scarcity of high-quality randomized controlled trials (RCTs) comparing antithrombotic regimens for secondary prevention of arterial thrombosis (AT) in antiphospholipid syndrome (APS). We reviewed different antithrombotic regimens used for this purpose. We searched for studies on management of AT in APS on PubMed and Web of Science. Eleven studies (5 RCTs, 3 prospective, and 3 retrospective cohort studies) comparing different regimens and reporting outcomes specifically for patients with index AT events were identified. Treatments were vitamin K antagonists (VKA; 9 studies), non-VKA oral anticoagulant (NOAC; 3 studies), single antiplatelet therapy (SAPT; 7 studies), dual antiplatelet therapy (DAPT; 2 studies), and VKA combined with SAPT (4 studies). We performed a meta-analysis for the outcomes: recurrent AT, any (arterial or venous) recurrent thromboembolism, and major bleeding. Recurrent AT was reduced with VKA plus SAPT versus VKA (risk ratio [RR]: 0.43; 95% confidence interval [CI]: 0.22-0.85) and with DAPT versus SAPT (RR: 0.29; 95% CI: 0.09-0.99). Any recurrent thromboembolism was reduced with VKA plus SAPT versus VKA alone (RR: 0.41; 95% CI: 0.24-0.69) and versus SAPT alone (RR: 0.36; 95% CI: 0.13-0.96). There were no significant differences between other treatments for thromboembolism and for none of the comparisons regarding major bleeding. In a sensitivity analysis, excluding low-quality studies, VKA was more effective than NOAC to prevent recurrent AT (RR: 0.25; 95% CI: 0.07-0.93). Combined antithrombotic therapy might be more effective than single agents as secondary prophylaxis in APS with AT, and does not seem to compromise with safety, but the quality of evidence is generally low. NOACs should be avoided for patients with APS and AT.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".