Prehospital hemorrhage management in low‐ and middle‐income countries: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Low- and middle-income countries (LMICs) account for 90% of deaths due to injury, largely due to hemorrhage. The increased hemorrhage mortality burden in LMICs is exacerbated by absent or ineffective prehospital care. Hemorrhage management (HM) is an essential component of prehospital care in LMICs, yet current practices for prehospital HM and outcomes from first responder HM training have yet to be summarized. METHODS: This review describes the current literature on prehospital HM and the impact of first responder HM training in LMICs. Articles published between January 2000 and January 2023 were identified using PMC, MEDLINE, and Scopus databases following PRISMA-ScR guidelines. Inclusion criteria spanned first responder training programs delivering prehospital care for HM. Relevant articles were assessed for quality using the Newcastle-Ottawa scale. RESULTS: Of the initial 994 articles, 20 met inclusion criteria representing 16 countries. Studies included randomized control trials, cohort studies, case control studies, reviews, and epidemiological studies. Basic HM curricula were found in 15 studies and advanced HM curricula were found in six studies. Traumatic hemorrhage was indicated in 17 studies while obstetric hemorrhage was indicated in three studies. First responders indicated HM use in 55%-76% of encounters, the most frequent skill they reported using. Mean improvements in HM knowledge acquisition post-course ranged from 23 to 58 percentage points following training for pressure and elevation, gauze application, and tourniquet application. CONCLUSIONS: Our study summarizes the current literature on prehospital HM in LMICs pertaining to epidemiology, interventions, and outcomes. HM resources should be a priority for further development.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".