Thromboelastography reflects global hemostatic variation among severe haemophilia A dogs at rest and following acute exercise
Bibliographic record
Abstract
The heterogeneity among severe haemophilia A patients reflects on variable tendencies for bleeding and also variable responses to FVIII therapy. This variability cannot be detected or predicted by routine coagulation tests. Thromboelastography (TEG) has recently been evaluated for assessing hemostatic patterns in haemophiliacs and proved valuable in monitoring therapy and/or prophylaxis, however, usually only in limited small case series. Exercise is an important component of overall haemophilia care, however, in severe haemophiliacs there is an increased risk of bleeding. The availability of a validated hemostatic test to evaluate the influence of exercise would be advantageous. This study has used TEG analysis to evaluate the global hemostatic status of a group of severe haemophilia A dogs at rest and after a standardized period of exercise. The study demonstrated significant inter and intra-individual variations based on TEG patterns at rest and following acute exercise as well as significant improvement of global hemostasis after exercise in the majority of tested dogs. The study supports the utilization of TEG in assessment of the hemostatic pattern in severe haemophilia A and provides a potential for utilizing TEG evaluation in managing exercise regimens for haemophilia care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".