PB0014 Do Antiphospholipid Antibodies Inform the Choice of Anticoagulant Agents in Patients with Atrial Fibrillation?
Bibliographic record
Abstract
Plasminogen activator inhibitor type 1 (PAI-1) and thrombin activatable fibrinolysis inhibitor (TAFI) were also determined.Ischemic stroke, major bleeding, and mortality were recorded during a median follow-up of 53 months while on anticoagulation.Results: Plasma PC (3.16 [2.54-3.99]nM/mg protein) at baseline showed association with age (r = 0.36, p < 0.0001) and CHA2DS2-VASc ( p = 0.003) but not with type of AF.Plasma PC was correlated with CLT (r = 0.34, p < 0.0001), and weakly with Ks (r = -0.15,p = 0.024), but not with fibrinogen, PAI-1 or TAFI levels.Of note, AF patients with left ventricular ejection fraction <40% (n = 57, 23.5%) had 22.7% higher ( p = 0.005) PC levels and CLT prolonged by 10% ( p = 0.044).Ischemic cerebrovascular events were observed in 20 patients (1.9%/year) who had at baseline 36.4% higher PC, 6% reduced Ks, and 15% longer CLT (all p < 0.05), also after adjustment for age.PC did not differ in patients who experienced major bleeding or death compared with the remainder.Conclusion(s): Our findings suggest that protein carbonylation contributes to the formation of more compact fibrin clots and impaired fibrinolysis in AF, probably due to posttranslational modifications of fibrinogen.We are the first to show that enhanced protein carbonylation in AF patients increases the risk of ischemic cerebrovascular events during anticoagulation, which highlights the role of oxidative stress in thrombotic manifestations of AF.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".