Testing for the lupus anticoagulant: the good, the bad, and the ugly
Bibliographic record
Abstract
Lupus anticoagulant (LA) represents 1 of the laboratory criteria for classification of patients as having definite antiphospholipid syndrome (APS). The other 2 laboratory criteria are anticardiolipin antibodies and anti-beta2-glycoprotein I antibodies. At least 1 of these antiphospholipid antibody (aPL) tests need to be positive, with evidence of persistence, together with evidence of at least 1 clinical criterion for APS, before a patient can be classified as having definite APS. LA and other aPL assays are also important for diagnosis or exclusion of APS, as well as for risk stratification, with triple-positive patients carrying the greatest risk. Whereas LA is identified through "uncalibrated" clot-based assays, the other aPL assays (anticardiolipin and anti-beta2-glycoprotein I antibodies) represent immunological assays, identified using calibrated solid-phase methods. Because LA is identified using clot-based assays, it is subject to considerable preanalytical and analytical issues that challenge accurate detection or exclusion of LA. In this narrative review, we take a look at the good, the bad, and the ugly of LA testing, primarily focusing on the last 10 years. Although harmonization of LA testing as a result of International Society on Thrombosis and Haemostasis guidance documents and other international activities has led to improvements in LA detection, many challenges remain. In particular, several anticoagulants, especially direct oral anticoagulants and also vitamin K antagonists, given as therapy to treat the pathophysiological consequences of aPL, especially thrombosis, interfere with LA assays and can generate false-positive or false-negative LA findings. Overcoming these diagnostic errors will require a multifaceted approach with clinicians and laboratories working together.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".