Molecular mechanisms underlying thrombosis in systemic lupus erythematosus – A Systematic review
Bibliographic record
Abstract
Patients with systemic lupus erythematosus (SLE) face an approximately 30 % risk of thrombosis post-diagnosis. However, there remains significant knowledge gaps regarding causative mechanisms, and there is a lack of specific antithrombotic guidelines. This systematic review aims to examine the existing literature regarding the mechanisms contributing to thrombosis risk in SLE, focusing on five predefined procoagulant domains: autoantibodies (including antiphospholipid antibodies (aPL)), the complement system, platelets, the endothelium, and the coagulation system. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statements and searched in PubMed and Embase without time restrictions. Risk of bias assessment was performed using a pre-specified evaluation tool. Out of 3,747 initially identified publications, 30 studies were included, with 28 demonstrating robust methodological quality in the risk of bias assessment. The studies were experimental, involving blood samples from cross-sectional SLE cohorts, except one animal -and one case-control study. We identified six different thrombosis mechanisms of action. Most studies concentrated on autoantibodies, predominantly aPL. Shared mechanisms between aPL and other autoantibodies may account for the increased thrombosis risk in aPL-negative SLE patients. Significant knowledge gaps remain, particularly regarding the role of the complement system in SLE-related thrombosis. Also, most research relies on cross-sectional designs, emphasizing the need for prospective cohort studies to better assess clinical factors. Finally, comprehensive studies examining the interactions between multiple procoagulant factors and their link to thrombosis are lacking. Closing these gaps in future research could improve both preventive and personalized treatment strategies for thrombosis in SLE.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".