Whole truths but half the blood: Addressing the gap between the evidence and practice of pre‐hospital and in‐hospital blood product use for trauma resuscitation
Bibliographic record
Abstract
BACKGROUND: In recent years, several studies have demonstrated the efficacy of using pre-hospital blood product and in-hospital whole blood for trauma resuscitation. While some observations suggest an encouraging uptake of this evidence by emergency medical service (EMS) agencies and trauma centers, a nationwide characterization of blood product utilization for bleeding trauma patients remains unknown. The objective of this study is to determine nationwide estimates of pre-hospital blood product and in-hospital whole blood utilization for trauma resuscitation. STUDY DESIGN AND METHODS: All adult trauma patients reported to the National Emergency Medical Services Information System (NEMSIS) dataset 2019 were included. Proportions of patients who received any pre-hospital blood product were calculated. The American College of Surgeons (ACS) Trauma Quality Programs (TQP) databases 2015-2017 and first quarter of 2020 were used to calculate the proportion of ACS-verified trauma centers that transfused whole blood. RESULTS: Among a total of 3,058,804 pre-hospital trauma patients, only 313 (0.01%) received any blood transfusion; 208 (0.21%) patients with systolic blood pressure (SBP) ≤90 mmHg and 121 (0.67%) patients with SBP ≤90 mmHg and heart rate ≥120 beats per minute received any blood product. The proportion of ACS-verified trauma centers transfusing whole blood increased from 16.7% (45/269) in 2015 to 24.5% (123/502) in first quarter of 2020. DISCUSSION: Despite strong evidence and recommendations, pre-hospital utilization of blood products for trauma resuscitation remains low. Additionally, while the overall in-hospital whole blood use also remains low, its use has increased at ACS-verified trauma centers over the past 5 years.
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 | 0.064 | 0.274 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".