Bibliographic record
Abstract
The balance between hemostasis and fibrinolysis can be lost in various situations, due to ineffective fibrinolysis in the setting of thrombosis or excessive fibrinolytic activity contributing to hemorrhage. Key players in the fibrinolytic system include plasminogen activators and inhibitors, plasmin and its precursor plasminogen. Disorders of fibrinolysis are challenging to diagnose due to limited availability of appropriate tests. D-dimer measurement is widely available, but does not allow evaluation of the balance between pro- and antifibrinolytic factors and hence cannot identify hypo- or hyperfibrinolysis. Individual assays of fibrinolytic proteins may be available at reference laboratories but currently, viscoelastic coagulation tests offer the best way to identify hypo- or hyperfibrinolysis at the point of care. When hyperfibrinolysis is identified or highly suspected, antifibrinolytic agents such as epsilon-aminocaproic acid and tranexamic acid can be used therapeutically. Although experience with these agents in veterinary medicine is limited, recent studies suggest they may be safe and effective in specific veterinary patient populations. Therapeutic thrombolysis is uncommonly performed in veterinary medicine, but involves administration of supraphysiological doses of tissue plasminogen activator to initiate fibrinolysis. This procedure carries significant risks of hemorrhage and reperfusion injury, but may be life-saving in selected cases.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".