Does cryosupernatant plasma improve outcome in thrombotic thrombocytopenic purpura? No answer yet
Bibliographic record
Abstract
A randomized prospective trial compared cryosupernatant plasma (CSP) to fresh frozen plasma (FFP) for treatment of thrombotic thrombocytopenic purpura (TTP). A total of 236 patients were required: 28 patients were treated with CSP and 24 with FFP within 30 months. There were no differences in survival at 1 month. By day 9, 17 of 26 patients with CSP and 18 of 24 with FFP had a platelet count >100 x 10(9)/l. At entry, von Willebrand factor (VWF) multimers were normal in all patients (range 1.1-3.95 IU/ml). ADAMTS-13 levels showed large variations ranging from 10% to 100% activity. At entry, no individual had <5% VWF cleaving protease. By day 9 (end of cycle), 89% (FFP) and 67% (CSP) had levels >50% of the controls. At 6 months some patients showed inhibitors to the enzyme in spite of adequate or normal platelet counts. The data from this study do not show an apparent advantage to the use of CSP in TTP. A large number of patients will be required to determine appropriate replacement therapy. We were not able to find a statistically significant relationship between the low level of protease activity at presentation of TTP and response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".