Management of immune thrombotic thrombocytopenic purpura with caplacizumab: a Canadian, single-centre, real-world experience
Bibliographic record
Abstract
When combined with therapeutic plasma exchange (TPE) and immunosuppression, upfront universal administration of caplacizumab was shown to be effective in the management of immune thrombotic thrombocytopenic purpura (iTTP). However, access to this drug remains challenging in many jurisdictions. We retrospectively review results of a single-institution experience with caplacizumab over a 3-year period. During the study period, we treated 48 patients with iTTP, of which 11 (23%) received caplacizumab. Eight of these 11 patients (73%) were female; the median age was 45 years (IQR 37.0-58.5). All received TPE within 24 h of admission (median 9 exchanges, IQR 7.0-12.5), and high-dose steroids. Caplacizumab was initiated for a median of 6 days after admission (IQR 2.5-8.0) and continued for a median of 26 days (IQR 14.0-33.0). Five patients (45%) had refractory disease at caplacizumab initiation. Ten patients (91%) survived, reaching clinical remission. Platelet normalization was reached with a median of 4 days following caplacizumab initiation (IQR 1.5-4.0). Complications included minor bleeding (n = 1) and local allergic reaction (n = 1). No patients experienced TTP exacerbation; relapse occurred in two patients (18%) over 1-5 years of follow-up. Caplacizumab appeared to be effective and safe, despite delayed initiation and in the setting of refractory disease.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".