Bibliographic record
Abstract
ABSTRACT: Acute hemorrhage can be a life-threatening emergency that is complex in its management and affects many patient populations. The past 15 years has seen the introduction of comprehensive massive hemorrhage protocols, wider use of viscoelastic testing, new coagulation factor products, and the publication of robust randomized controlled trials in diverse bleeding patient populations. Although gaps continue to exist in the evidence base for several aspects of patient care, there is now sufficient evidence to allow for an individualized hemostatic response based on the type of bleeding and specific hemostatic defects. We present 3 clinical cases that highlight some of the challenges in acute hemorrhage management, focusing on the importance of interprofessional communication, rapid provision of hemostatic resuscitation, repeated measures of coagulation, immediate administration of tranexamic acid, and prioritization of surgical or radiologic control of hemorrhage. This article provides a framework for the clear and collaborative conversation between the bedside clinical team and the consulting hematologist to achieve prompt and targeted hemostatic resuscitation. In addition to providing consultations on the hemostatic management of individual patients, the hematology service must be involved in setting hospital policies for the prevention and management of patients with major hemorrhage.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.019 |
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".