A Quarter of a Century in Anticardiolipin Antibody Testing and Attempted Standardization Has Led Us to Here, which Is?
Bibliographic record
Abstract
The anticardiolipin (aCL) test has been widely used by physicians since the mid-1980s for diagnosing patients with antiphospholipid syndrome (APS). Establishment of this diagnosis has enabled effective management of patients with recurrent thrombosis and recurrent pregnancy losses. The test was first established in 1983 as a radioimmunoassay and soon thereafter converted into an enzyme-linked immunosorbent assay (ELISA). The other test commonly used in the diagnosis of APS is the lupus anticoagulant (LA) test. The aCL ELISA is sensitive for the diagnosis of APS but lacks specificity. On the other hand, the LA assay, although more specific, is not as sensitive as the aCL ELISA. More specific tests are now available such as the anti-beta2 glycoprotein I (anti-beta2GPI) assay, the antiprothrombin assay, and other ELISAs that use negatively charged phospholipids instead of cardiolipin to coat the plates. In the past 25 years, there have been numerous efforts to standardize aCL, LA, and anti-beta2GPI tests but there are still reports of significant intra- and interlaboratory variation in results for all three assays. This article discusses in detail the clinical value of these tests, technical problems associated with their use, the current laboratory classification criteria for diagnosis of APS, and possible new and better assays that will be available in the near future for diagnosis of APS.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".