Global Emergency Medicine: A Review of the Literature From 2015
Bibliographic record
Abstract
OBJECTIVES: The Global Emergency Medicine Literature Review (GEMLR) conducts an annual search of peer-reviewed and gray literature relevant to global emergency medicine (EM) to identify, review, and disseminate the most important new research in this field to a global audience of academics and clinical practitioners. METHODS: This year 12,435 articles written in six languages were identified by our search. These articles were distributed among 20 reviewers for initial screening based on their relevance to the field of global EM. An additional two reviewers searched the gray literature. A total of 723 articles were deemed appropriate by at least one reviewer and approved by their editor for formal scoring of overall quality and importance. Two independent reviewers scored all articles. RESULTS: A total of 723 articles met our predetermined inclusion criteria and underwent full review. Sixty percent were categorized as emergency care in resource-limited settings (ECRLS), 17% as EM development (EMD), and 23% as disaster and humanitarian response (DHR). Twenty-four articles received scores of 18.5 or higher out of a maximum score 20 and were selected for formal summary and critique. Inter-rater reliability between reviewers gave an intraclass correlation coefficient of 0.71 (95% confidence interval = 0.66 to 0.75). Studies and reviews with a focus on infectious diseases, trauma, and the diagnosis and treatment of diseases common in resource-limited settings represented the majority of articles selected for final review. CONCLUSIONS: In 2015, there were almost twice as many articles found by our search compared to the 2014 review. The number of EMD articles increased, while the number ECRLS articles decreased. The number of DHR articles remained stable. As in prior years, the majority of articles focused on infectious diseases.
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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.038 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".