Variability of thromboelastographic responses following the administration of rFVIIa to haemophilia A dogs supports the individualization of therapy with a global test of haemostasis
Bibliographic record
Abstract
The efficacy of recombinant factor VIIa (rFVIIa) therapy in haemophilia A is challenged by the lack of a reliable monitoring tool for treatment response. This is further complicated by the significant inter-patient variability associated with this response. Thromboelastography (TEG), a real time global haemostatic test has shown superiority over conventional tests of haemostasis and has proven efficiency in the monitoring of bypass agents such as rFVIIa and FEIBA™. However, this evaluation has been limited to a few case studies or very small patient series. In this study, six severe haemophilia A dogs were treated with a clinically relevant single dose of rFVIIa, and therapy was monitored by thromboelastography predrug and at 15, 30 and 60 min postdrug administration using citrated whole blood samples activated with tissue factor and compared with non-tissue factor-activated samples. Despite the homogeneity of the tested dogs, a clear inter-individual variation was observed in the pre-and post-rFVIIa Thromboelastography analyzes. The improvement of global haemostatic parameters was seen as early as 15 min following drug administration, with a peak for factor VIIa activity in plasma at the same time. There is a significant correlation between plasma FVIIa and TEG parameters 15 min postinjection, and the baseline TEG profile influences the individual postdrug administration outcome. Together, these data support the value of TEG not only as an effective monitoring haemostatic test, but also as a tool for individualization of therapy to achieve the best haemostatic and cost effectiveness of rFVIIa therapy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".