Thrombin generation abnormalities in Quebec platelet disorder
Bibliographic record
Abstract
INTRODUCTION: Calibrated automated thrombograms (CAT) with platelet-poor (PPP) and platelet-rich plasma (PRP) have provided useful insights on bleeding disorders. We used CAT to assess thrombin generation (TG) in Quebec platelet disorder (QPD)-a bleeding disorder caused by a PLAU duplication mutation that increases platelet (but not plasma) urokinase plasminogen activator (uPA), leading to intraplatelet (but not systemic) plasmin generation that degrades α-granule proteins and causes platelet (but not plasma) factor V (FV) deficiency. METHODS: Calibrated automated thrombograms was used to test QPD (n = 7) and control (n = 22) PPP and PRP, with or without added tranexamic acid (TXA). TG endpoints were evaluated for relationships to platelet FV and uPA, plasma FV and tissue factor pathway inhibitor (TFPI) levels, and bleeding scores. RESULTS: ≥ 0.81), but unrelated to platelet uPA, plasma FV, or bleeding scores. QPD TG abnormalities were not associated with TFPI abnormalities and were not reproduced by adding uPA to control PRP. TXA increased QPD and control PRP TG more than PPP TG, but it did not fully correct QPD PRP TG abnormalities or improve TG by plasminogen-deficient plasma. CONCLUSION: Quebec platelet disorder results in a platelet-specific TG defect, proportionate to the loss of platelet FV, that is improved but not fully corrected by TXA. Our study provides an interesting example of why it is important to assess both PRP and PPP TG in bleeding disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".