Global emergency medicine: A scoping review of the literature from 2023
Bibliographic record
Abstract
OBJECTIVE: The Global Emergency Medicine Literature Review (GEMLR) highlights the highest-quality research addressing emergency care in resource-limited settings (ECRLS). This 18th edition reviews global emergency medicine (GEM) literature published during 2023. METHODS: A scoping review of GEM articles published in 2023 was performed using a systematic PubMed search and manual gray literature (GRAY) search. Reviewers and editors from 10 countries screened articles utilizing case definitions of three categories of GEM research-disaster and humanitarian response (DHR), ECRLS, and emergency medicine development (EMD). After duplicates and those not meeting authorship equity and ethical review requirements were removed, articles were scored according to rubrics for original research (OR), review articles (RE), and GRAY. Those in the top 5% from any category were summarized and critiqued in narrative review. RESULTS: There were 58,291 articles identified in the main search and 11,035 in the GRAY search. A total of 825 articles from the main search and 37 GRAY articles screened in and were scored. Fifty-five main search articles and one GRAY article were included after scoring, a 52.8% increase from 2022 despite <1% change in search volume. ECRLS remained the largest category (63%). As in previous years, articles frequently addressed emergencies in pediatrics (10 articles), trauma (9), prehospital care (8), maternal/neonatal care (6), education/training (6), disaster medicine (4), and airway/sedation management (4). A total of 3.5% of screened-in articles failed to meet GEMLR's new authorship equity and ethics standards. CONCLUSIONS: The quantity and quality of GEM research continues to grow as measured by the GEMLR scoring system. A revised search string identified relevant GEM articles with broad application in global settings. New equity guidelines were successfully implemented. This review summarizes the highest quality current GEM research while providing evolving guidelines for best practices in performing this important and rapidly growing work.
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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 | 0.027 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.048 | 0.030 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".