Bibliographic record
Abstract
PURPOSE OF REVIEW: Bleeding in a perioperative setting occurs due to multiple causes, but newer anticoagulant and antiplatelet therapies are increasingly used preoperatively. As a result, patients often can present for surgery with underlying hemostatic disorders due to these acquired disorders or following major surgery or trauma. Because bleeding occurs due to multiple causes, the addition of pharmacologic agents creates an acquired defect that complicates the surgical injury and may result in increased blood loss. An understanding of hemostasis and therapeutic approaches, especially those in our current clinical settings, is crucial in managing these patients. RECENT FINDINGS: Pharmacologic agents including antifibrinolytics and prohemostatic proteins are commonly administered, but a multimodal approach to management is important. Of note is that aprotinin has been reintroduced into the Canadian market in September 2011. Recombinant and purified coagulation therapies are under investigation, and provide clinicians specific agents to treat targeted deficiencies. SUMMARY: Nonsurgical bleeding in the operating room is the result of a multitude of factors including preoperative anticoagulants, dilution, fibrinolysis, and factor consumption. Therapeutic prohemostatic pharmacologic approaches, in addition to standard transfusion therapy, need to be considered in the prevention and treatment of coagulopathy in surgical patients.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".