Ruling out lupus anticoagulants with mixing test–specific cutoff assessment and the index of circulating anticoagulant
Bibliographic record
Abstract
BACKGROUND: Lupus anticoagulant (LA) is classified in the antibody family that is recognized in antiphospholipid syndrome. Mixing tests are recommended for LA detection, and either a mixing test-specific cutoff (MTC) or index of circulating anticoagulant (ICA) is used for the interpretation. Although we previously showed MTC had higher sensitivity for LA than ICA, there are few studies investigating specificity. OBJECTIVES: To investigate specificity of multiple activated partial thromboplastin time (APTT) and diluted Russell's viper venom time (dRVVT) reagents for inhibitors using plasmas with non-LA causes of prolonged clotting times, interpreted with MTC and ICA. METHODS: Seventy-six factor-deficient samples (either artificially prepared, hereditary deficiency, or warfarin), and 12 inhibitors (either coagulation factor inhibitors, rivaroxaban, or apixaban) were used. Samples were tested with 4 APTTs, 1 dilute APTT (dAPTT), and 2 dRVVT reagents, and all elevated screen ratios were followed up with mixing tests. Frequencies of corrected and not-corrected results were calculated. RESULTS: The frequency of MTC and ICA corrected results, suggesting factor deficiency, were 5% to 43% and 79% to 100%, respectively, except for dAPTT, where MTC and ICA performed similarly. Frequencies of MTC and ICA not-corrected results, suggesting inhibition, were 29% to 100% and 25% to 67%, respectively. CONCLUSIONS: The data indicate that MTC has a tendency to generate not-corrected mixing tests in factor-deficient, warfarin, and other inhibitor samples, while ICA exhibited higher specificity. When we perform the mixing test and interpret the data, it is important to understand the characteristics of the indexes for maximizing the diagnostic potential of mixing test.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".