Global emergency medicine: A scoping review of the literature from 2021
Bibliographic record
Abstract
OBJECTIVE: The objective was to identify the most important and impactful peer-reviewed global emergency medicine (GEM) articles published in 2021. The top articles are summarized in brief narratives and accompanied by a comprehensive list of all identified articles that address the topic during the year to serve as a reference for clinicians, researchers, and policy makers. METHODS: A systematic PubMed search was carried out to identify all GEM articles published in 2021. Title and abstract screening was performed by trained reviewers and editors to identify articles in one of three categories based on predefined criteria: disaster and humanitarian response (DHR), emergency care in resource-limited settings (ECRLS), and emergency medicine development (EMD). Included articles were each scored by two reviewers using established rubrics for original (OR) and review (RE) articles. The top 5% of articles overall and the top 5% of articles from each category (DHR, ECRLS, EMD, OR, and RE) were included for narrative summary. RESULTS: The 2021 search identified 44,839 articles, of which 444 articles screened in for scoring, 25% and 22% increases from 2020, respectively. After removal of duplicates, 23 articles were included for narrative summary. ECRLS constituted the largest category (n = 16, 70%), followed by EMD (n = 4, 17%) and DHR (n = 3, 13%). The majority of top articles were OR (n = 14, 61%) compared to RE (n = 9, 39%). CONCLUSIONS: The GEM peer-reviewed literature continued to grow at a fast rate in 2021, reflecting the continued expansion and maturation of this subspecialty of emergency medicine. Few high-quality articles focused on DHR and EMD, suggesting a need for further efforts in those fields. Future efforts should focus on improving the diversity of GEM research and equitable representation.
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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 | 0.034 | 0.094 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.052 | 0.028 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".