Bibliographic record
Abstract
Abstract Strategies to reduce blood loss and the need for transfusions in surgery include enhancement of coagulation, inhibition of fibrinolysis, and an improved decision algorithm for transfusion based on bedside monitoring of global hemostasis. The synthetic antifibrinolytic drug tranexamic acid has emerged as an effective alternative in this respect for orthopedic and cardiac surgery. Although it seems less effective than aprotinin, it has not been associated with the increased risk of mortality of the latter. Thromboelastography to monitor the global hemostatic capacity and to guide the appropriate use of blood components in cardiac surgery is also effective in reducing the need for transfusion. Patients on antithrombotic drug therapy may need reversal before surgery to avoid excessive blood loss, or intraoperatively in cases of unexpected bleeding. Available options are protamine for unfractionated or low-molecular-weight heparin, recombinant activated factor VII for fondaparinux, prothrombin complex concentrate for vitamin K antagonists and possibly for oral factor Xa inhibitors, dialysis and possibly activated prothrombin complex concentrate for oral thrombin inhibitors, desmopressin for aspirin and possibly for thienopyridines, and platelet transfusions for the latter.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".