Treatment of acquired Thrombotic Thrombocytopenic Purpura in the U.S. remains heterogeneous: Current and future points of clinical equipoise
Bibliographic record
Abstract
BACKGROUND: The purpose of this survey was to describe current practices in the U.S. for treatment of acquired Thrombotic Thrombocytopenic Purpura (TTP), compare these with prior U.S. and current Canadian practices, and identify areas of clinical equipoise. STUDY DESIGN AND METHODS: A research team member administered the survey by telephone. Questions included an estimate of the annual patient volume treated, apheresis and medical therapy practices for acquired TTP. RESULTS: /L (66%), and then TPE is tapered off (69%). Compared with a U.S. survey from 1998, a greater proportion of centers use plasma exclusively as the replacement fluid exclusively (29/32 vs 2/14 in 1998; P < .0001) and taper TPE (22/32 vs 8/20 in 1998, P = .0499). Compared with Canadian survey in 2016, a greater proportion of U.S. centers use plasma over cryosupernatant (29/32 vs 2/13 CAG centers, P < .0001) and initiate TPE with 1.0 PV compared with 1.5 PV (23/32 vs 0/14 CAG centers, P < .0001). Corticosteroid use is common but not universal (U.S. and CAG) and use of rituximab heterogeneous. CONCLUSION: Treatment of acquired TTP in the U.S. remains heterogeneous. Points of clinical equipoise identified were PV exchanged (1.0 vs >1.0), tapering of TPE versus none, and rituximab use.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".