Effective tranexamic acid concentration for 95% inhibition of tissue-type plasminogen activator-induced hyperfibrinolysis in full-term pregnant women: a prospective interventional study
Bibliographic record
Abstract
Postpartum haemorrhage is the leading cause of maternal mortality and morbidity worldwide. Tranexamic acid (TXA) has been shown to reduce blood loss and blood product transfusion requirements. Despite clinical evidence, further studies are needed to better define the pharmacokinetic and pharmacodynamic characteristics of TXA in pregnant women. The objective of our prospective observational ex-vivo study was to define the effective TXA concentration required to inhibit 95% (EC95) of tissue-type plasminogen activator (t-PA)-induced fibrinolysis in full-term pregnant women. Hyperfibrinolysis was induced by adding supraphysiologic concentration of t-PA to blood samples obtained from 30 full-term pregnant women and 10 healthy nonpregnant female volunteers. Increasing TXA concentrations (0--40 μg/ml) were then spiked into the blood samples and inhibition of fibrinolysis was assessed using the lysis index at 30 min of the ROTEM measured on EXTEM and NATEM tests. Effective TXA concentrations required to achieve EC95 were extrapolated using nonlinear regression. EC95 were compared between groups using an extra sum-of-squares F test. EC95 in pregnant women was 14.7 μg/ml (95% CI 12.4--17.5 μg/ml) on EXTEM and 11.2 μg/ml (95% CI 8.3--15.1 μg/ml) on NATEM tests. These values were significantly higher than those obtained in volunteers: 8.7 μg/ml (95% CI 5.5--13.9 μg/ml) and 6.8 μg/ml (95% CI 5.3--8.8 μg/ml), respectively (both P < 0.001). Our results suggest a higher fibrinolytic potential in pregnant women compared with nonpregnant women.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".