Massive haemorrhage protocol: what's the best protocol?
Bibliographic record
Abstract
Massive transfusion protocols became common practice between 2006 and 2010. The terminology of ‘massive transfusion protocol’ was improved to ‘massive haemorrhage protocol ( MHP )’ with the astute recognition that at the start of such a protocol, it is unclear which patients will meet the definition of massive transfusion (10 U/24 h). Despite the majority of the literature being reported from the trauma population, hospitals have generally adopted a single MHP for all patients. It remains unclear whether the same protocol can be used for all patients. MHP s assist with the prevention and management of the acute coagulopathy of trauma/shock ( ACOTS ). The goals of a MHP are to improve haemostasis, communication and patient outcomes. The protocol must be specific for an individual hospital, depending on factors such as prehospital transport times, distance from laboratory to trauma and operating rooms, patient populations served and types of tests available. The key components of a MHP are the 6Ts: t riggering of the protocol, laboratory t esting, t ranexamic acid, t emperature maintenance, t ransfusion support and t ermination of the protocol when haemostasis is achieved. The evidence and importance of each of these steps will be discussed in detail. It is also critical that a quality assurance programme supports the MHP . Poor compliance with the institutional MHP is associated with inferior survival. Each MHP activation should be followed by a formal debrief by the team. Audits should be performed to determine compliance and to inform annual update of the MHP . Formal training and/or simulation should be a core part of the policy.
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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 | 0.154 | 0.303 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.033 | 0.023 |
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".