Evidence for a role of anti-ADAMTS13 autoantibodies despite normal ADAMTS13 activity in recurrent thrombotic thrombocytopenic purpura
Bibliographic record
Abstract
BACKGROUND: Severe ADAMTS13 deficiency is a critical component of the pathogenesis of idiopathic thrombotic thrombocytopenic purpura but is found only in about 60% of patients clinically diagnosed with this disease. DESIGN AND METHODS: Over a period of 8 years and six episodes of thrombotic thrombocytopenic purpura we studied the evolution of the anti-ADAMTS13 antibody response in a patient using different ADAMTS13 assays and epitope mapping. RESULTS: Anti-ADAMTS13 autoantibodies were found in all episodes but were inhibitory only in the last two episodes. In a flow-based assay, normal ADAMTS13 activity was found only during the first disease episode, while ADAMTS13 activity was normal using a static assay in episodes 1 and 3, and severely deficient in the last two episodes. Fluorescence evolution in a modified fluorescence resonance energy transfer assay using a von Willebrand factor A2 domain peptide substrate was linear in episodes 1, 5 and 6, but increased exponentially in episodes 3 and 4. Despite the variable functional characteristics of the anti-ADAMTS13 autoantibodies, their principal epitope was the ADAMTS13 spacer domain in all episodes. CONCLUSIONS: The patient is unique as he displayed features of maturation or shaping of the anti-ADAMTS13 autoantibody response during the course of multiple episodes of thrombotic thrombocytopenic purpura. Anti-ADAMTS13 autoantibodies may be important in vivo despite normal ADAMTS13 activity in routine assays. Consequently, treatment decisions should not be based solely on activity assay results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".