Anticardiolipin Antibody and Anti-beta 2 Glycoprotein I Antibody Assays
Bibliographic record
Abstract
Antiphospholipid syndrome (APS) is an autoimmune disease and is a risk factor for a number of clinical manifestations; the classic presentations include fetal death or thrombosis (arterial or venous thromboembolism), in the presence of persistently increased titers of antiphospholipid (aPL) antibodies. The actual cause of APS is unknown but thought to be multifactorial. The disease is characterized by the presence of a heterogenous population of autoantibodies against phospholipid-binding proteins. APS presents either in isolation with no evidence of an underlying disease or in concert with an autoimmune disease such as systemic lupus erythematosus or rheumatoid arthritis. The wide diversity in clinical presentation often causes difficulty in identifying and treating patients and therefore a concise laboratory report containing interpretative comments is required to provide needed guidance to the clinician. For a diagnosis of APS to be made both clinical and laboratory classification criteria must be met. Laboratory testing to identify aPL antibodies includes lupus anticoagulant (liquid-based clotting assays) and immunological solid-phase assays (usually enzyme-linked immunosorbent assay formats) for IgG and/or IgM anticardiolipin (aCL) antibodies and anti-beta 2 glycoprotein I (β2-GPI) antibodies. Other autoantibodies, such as those directed against anionic phospholipids, can also be assayed; however they are not of clinical significance. Participation in a quality assurance program and an in-depth technical and clinical understanding of testing for aPL antibodies are required, as methods are limited by poor robustness, reproducibility, specificity, and standardization. Testing is further complicated by the lack of a "gold standard" laboratory test to diagnose or classify a patient as having APS. This chapter discusses the clinical and laboratory theoretical and technical aspects of aCL and anti-β2GPI antibody assays.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".