How we treat thrombotic thrombocytopenic purpura: Results of a Canadian TTP practice survey
Bibliographic record
Abstract
BACKGROUND: Thrombotic thrombocytopenic purpura (TTP) is a rare disease with 90% mortality if untreated. Since the Canadian Apheresis Group (CAG) trial showed greater survival with therapeutic plasma exchange (TPE) versus plasma infusion, there has been widespread adoption of TPE. Beyond TPE, there is significant practice variation. To characterize this, we developed a survey sent to physicians who might be directly involved in TTP management. METHODS: test was used to compare respondents who were and were not CAG physicians. We also compared responses by estimated frequency of TTP cases per year. RESULTS: The CAG response rate was 31% (13 of 42). The survey was sent to 665 non-CAG physicians, of whom 41 responded (6.1%). Though not statistically different, CAG and non-CAG respondents varied regarding use of corticosteroids, aspirin, and venous thromboembolism (VTE) prophylaxis. Significant differences were found between CAG and non-CAG groups regarding cryosupernatant as fluid choice (69.2% vs. 22.5%, P = .004) and the use of TPE tapering (84.6% vs. 51.3%, P = .034), respectively. CONCLUSION: TTP treatment is variable across centres in Canada. Areas of significant variation include the choice of replacement fluid for TPE and whether or not and how to taper TPE. Our survey highlights the practice heterogeneity that exists and identifies areas where more evidence is needed and perhaps where trials should be performed.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 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".