How we diagnose and treat thrombotic manifestations of the antiphospholipid syndrome: a case-based review
Bibliographic record
Abstract
Antiphospholipid antibodies including anticardiolipin antibodies, lupus anticoagulants, and anti-beta(2) glycoprotein-1-specific antibodies may identify patients at elevated risk of first or recurrent venous or arterial thromboembolism. Traditionally, published case series supplemented by anecdotal experience have formed the basis of management of patients with these autoantibodies. Over the past several years, studies have described the management of patients with key clinical manifestations of antiphospholipid antibodies, including patients with antiphospholipid antibody syndrome. As a result, evidence-based treatment recommendations are possible for selected patients with, or at risk of, thrombosis in the setting of antiphospholipid antibodies. Unfortunately, most patients encountered in clinical practice do not correspond directly with those enrolled in clinical trials. For such patients, treatment recommendations are based on experience, extrapolation, and less rigorous evidence. This article proposes 5 cases typical of those found in clinical practice and provides recommendations for therapy focused on a series of clinical questions. Whenever possible, the recommendations are based on evidence; however, in many cases, insufficient evidence exists, so the recommendation is experiential.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".