Bibliographic record
Abstract
Blood loss and subsequent transfusions are associated with major morbidity and mortality. The use of antifibrinolytics can reduce blood loss in cardiac surgery, trauma, orthopedic surgery, liver surgery and solid organ transplantation, obstetrics and gynecology, neurosurgery and non-surgical diseases. The evidence of their efficacy has been mounting for years. Tranexamic acid (TXA), a synthetic lysine-analogue antifibrinolytic, was first patented in 1957 and its use has been increasing in contrast to aprotinin, a serine protease inhibitor antifibrinolytic. This review aims to help acute care physicians navigate through the clinical evidence available for TXA therapy, develop appropriate dose regimens whilst minimizing harm, as well as understand its broadening scope of applications. Many questions remain unanswered regarding other clinical effects of TXA such as anti-inflammatory response to cardiopulmonary bypass, the risk of thromboembolic events, adverse neurological effects such as seizures, and its morbidity and mortality, all of which necessitate further clinical trials on its usage and safety in various clinical settings.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".