Global Emergency Medicine: A Scoping Review of the Literature From 2024
Bibliographic record
Abstract
OBJECTIVE: The Global Emergency Medicine Literature Review (GEMLR) identifies impactful research in global emergency care. This 20th annual edition reviews GEM literature published in 2024 and highlights the growth of GEMLR over the years. METHODS: We conducted a scoping review of 2024 GEM articles through structured PubMed and gray literature searches. Reviewers and editors from 10 countries screened abstracts using predefined criteria for three domains: disaster and humanitarian response (DHR), emergency care in limited-resource settings (ECLRS), and emergency medicine development (EMD). Duplicates and articles lacking authorship equity or ethical oversight were excluded. Remaining publications were scored using tailored rubrics for original research (OR), review articles (RE), and gray literature (GRAY). The top 5% in each category were selected for critical appraisal. A retrospective summary of 20 years of GEMLR reviews was also completed. RESULTS: The search identified 46,714 PubMed and 12,575 gray literature articles. A total of 473 met inclusion criteria and were scored; 33 were selected as the top 5%, a decrease from 55 in 2023. Although the search string was unchanged, 2024 yielded ~10,000 fewer articles. Common themes included trauma, pediatrics, and clinical/triage protocols, with a new focus on mental health among clinicians and disaster victims. Over 20 years, 230 individuals have contributed 810 service-years to GEMLR. Nearly 75% of members were from the USA, while 32 (13.9%) were from LMICs and 27 (11.7%) from non-USA high-income countries. In total, 398,904 articles have been screened, 8476 scored, and 517 top articles narratively reviewed since 2005. CONCLUSIONS: Over two decades, GEMLR has evolved into a large-scale, multinational, methodologically rigorous initiative, highlighting more than 500 high-impact GEM publications. In 2024, despite fewer articles screened, 33 top studies were identified across key domains. GEMLR emphasizes equitable LMIC representation, rigorous quality standards, and authorship equity, aiming to help shape the future of emergency care research.
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 | 0.024 | 0.075 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.049 | 0.032 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".