The influence of prophylactic factor VIII in severe haemophilia A
Bibliographic record
Abstract
Haemophilia A individuals displaying a similar genetic defect have heterogeneous clinical phenotypes. Our objective was to evaluate the underlying effect of exogenous factor (f)VIII on tissue factor (Tf)-initiated blood coagulation in severe haemophilia utilizing both empirical and computational models. We investigated twenty-five clinically severe haemophilia A patients. All individuals were on fVIII prophylaxis and had not received fVIII from 0.25 to 4 days prior to phlebotomy. Coagulation was initiated by the addition of Tf to contact-pathway inhibited whole blood ± an anti-fVIII antibody. Aliquots were quenched over 20 min and analyzed for thrombin generation and fibrin formation. Coagulation factor levels were obtained and used to computationally predict thrombin generation with fVIII set to either zero or its value at the time of the draw. As a result of prophylactic fVIII, at the time of the blood draw, the individuals had fVIII levels that ranged from <1% to 22%. Thrombin generation (maximum level and rate) in both empirical and computational systems increased as the level of fVIII increased. FXIII activation rates also increased as the fVIII level increased. Upon suppression of fVIII, thrombin generation became comparable in both systems. Plasma composition analysis showed a negative correlation between bleeding history and computational thrombin generation in the absence of fVIII. Residual prophylactic fVIII directly causes an increase in thrombin generation and fibrin cross-linking in individuals with clinically severe haemophilia A. The combination of each individual's coagulation factors (outside of fVIII) determine each individual's baseline thrombin potential and may affect bleeding risk.
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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".