Current management of thrombotic thrombocytopenic purpura
Bibliographic record
Abstract
PURPOSE OF REVIEW: New treatment modalities have become increasingly popular for the treatment of acute thrombotic thrombocytopenic purpura. Widespread availability of ADAMTS13 assays resulted in the increased recognition of patients with hereditary thrombotic thrombocytopenic purpura and specific issues related to acquired ADAMTS13 deficiency. These new aspects with implications on management of thrombotic thrombocytopenic purpura patients are reviewed here. RECENT FINDINGS: Today, plasma exchange with the replacement of fresh frozen plasma is still the treatment of choice in acute thrombotic thrombocytopenic purpura. The finding of circulating anti-ADAMTS13 autoantibodies in the majority of patients constitutes the rationale for the concomitant administration of immunosuppressive drugs. Rituximab seems to have a favorable benefit-risk ratio in plasma-refractory and relapsing thrombotic thrombocytopenic purpura; however, long-term follow-up data are not yet available. Constitutively lacking ADAMTS13 in hereditary thrombotic thrombocytopenic purpura can be supplemented by simple plasma infusions. Severe acquired ADAMTS13 deficiency either at presentation or in remission identifies patients at a particularly high risk of relapse. SUMMARY: Despite progress in understanding the pathophysiology of thrombotic thrombocytopenic purpura, acute bouts as well as relapses still represent serious health threats to patients and rapid initiation of plasma exchange is mandatory. Large randomized clinical trials, however, need to determine whether new treatment modalities are superior to standard plasma exchange.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".