Global Emergency Medicine: A Scoping Review of the Literature from 2022
Bibliographic record
Abstract
OBJECTIVE: The objective was to identify the highest quality global emergency medicine (GEM) research published in 2022. The top articles are compiled in a comprehensive list of all the year's GEM articles and narrative summaries are performed on those included. METHODS: A systematic PubMed search was conducted to identify all GEM articles published in 2022 and included a manual supplemental screen of 11 organizational websites for gray literature (GRAY). A team of trained reviewers and editors screened all identified titles and abstracts, based on three case definition categories: disaster and humanitarian response (DHR), emergency care in resource-limited settings (ECRLS), and emergency medicine development (EMD). Articles meeting these definitions were independently scored by two reviewers using rubrics for original research (OR), review (RE) articles, and GRAY. Articles that scored in the top 5% from each category as well as the overall top 5% of articles were included for narrative summary. RESULTS: The 2022 search identified 58,510 articles in the main review, of which 524 articles screened in for scoring, respectively, 30% and 18% increases from last year. After duplicates were removed, 36 articles were included for narrative summary. The GRAY search identified 7755 articles, of which 33 were scored and one was included for narrative summary. ECRLS remained the largest category (27; 73%), followed by DHR (7; 19%) and EMD (3; 8%). OR articles remained more common than RE articles (64% vs. 36%). CONCLUSIONS: The waning of the COVID-19 pandemic has not affected the continued growth in GEM literature. Articles related to prehospital care, mental health and resilience among patients and health care workers, streamlining pediatric infectious disease care, and disaster preparedness were featured in this year's review. The continued lack of EMD studies despite the global growth of GEM highlights a need for more scholarly dissemination of best practices.
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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 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.057 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 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".