Barriers and Challenges to Implementing Whole Blood Transfusion Protocols in Civilian Hospitals: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Whole blood is a product that contains all three blood components (plasma, red blood cells, and platelets). This systemic review and meta-analysis was conducted to identify barriers and obstacles to establishing whole blood transfusion protocols in civilian hospitals. Methods: The study was conducted using PRISMA guidelines with PROSPERO registration No. CRD42024519898. Traumatic patients who needed or received whole blood transfusion were included. A systematic literature review employed a comprehensive search strategy through the PubMed, Google Scholar, Web of Science, ScienceDirect, and ProQuest databases. Meta-analysis was utilized to analyze the outcomes. The risk of bias was assessed using the Newcastle–Ottawa Scale. Results: In total, 310 studies were identified, and 11 studies met the inclusion criteria. The following intervals were used to assess the prevalence of mortality: 6 h 12.15% (0.081, 95% CI [0.023, 0.139]), 24 h 14.08% (0.141, 95% CI [0.111, 0.171]), delayed mortality (28–30 days) 22.89% (0.284, 95% CI [0.207, 0.360]), and in-hospital 18.72%, with relative risk (0.176, 95% CI [0.114,0.238]). Conclusions: Traumatic patients can be effectively resuscitated and stabilized with whole blood transfusion (WBT), but it is essential to provide ongoing critical care, address logistical challenges, and prevent blood product wastage. We recommend utilizing WBT in the early stages of resuscitation for adult civilian trauma 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 | 0.033 | 0.079 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".