Prehospital and Emergency Care: Updates from the Disease Control Priorities, Version 3
Bibliographic record
Abstract
BACKGROUND: It is increasingly understood that emergency care systems can be cost-effective in low- and middle-income countries (LMICs). The development of such systems, however, is still a work in progress. This article updates previous work in providing the most recent estimates of the burden of disease sensitive to emergency care, the current state of knowledge on the feasibility of emergency care, effect on outcomes, and cost-effectiveness in LMICs, and future directions for research, policy, and implementation. METHODS: We calculated the potential impact of prehospital and emergency care systems using updated and revised data based on the global burden of disease study. We then assessed the state of current knowledge and potential future directions for research and policy by conducting a review of the literature on current systems in LMICs. RESULTS: According to these newest updates, 24 million deaths related to emergency medical conditions occur in LMICs annually, accounting for an estimated 932 million years of life lost. Evidence shows that multiple emergency care models can function in different local settings, depending on resources and urbanicity. Emergency care can significantly improve mortality rates from emergent conditions and be highly cost-effective. Further research is needed on implementation of emergency care systems as they become a necessary reality in developing nations worldwide. CONCLUSIONS: Emergency care implementation in LMICs presents both challenges and opportunities. Investment in evidence-based emergency care, research on implementation, and system coordination in LMICs could lead to a more cost- and outcome-effective emergency care system than exists in advanced economies.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.042 | 0.014 |
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".