Global Emergency Medicine: A Review of the Literature From 2013
Bibliographic record
Abstract
OBJECTIVES: The Global Emergency Medicine Literature Review (GEMLR) conducts an annual search of peer-reviewed and grey literature relevant to global emergency medicine (EM) to identify, review, and disseminate the most important new research in this field to a worldwide audience of academics and clinical practitioners. METHODS: This year 8,768 articles written in six languages were identified by our search. These articles were distributed among 22 reviewers for initial screening based on their relevance to the field of global EM. An additional two reviewers searched the grey literature. A total of 434 articles were deemed appropriate by at least one reviewer and approved by an editor for formal scoring of overall quality and importance. RESULTS: Of the 434 articles that met our predetermined inclusion criteria, 65% were categorized as emergency care in resource-limited settings, 18% as EM development, and 17% as disaster and humanitarian response. A total of 24 articles received scores of 18 or higher and were selected for formal summary and critique. Interrater reliability for two reviewers using our scoring system was good, with an intraclass correlation coefficient of 0.63 (95% confidence interval = 0.55 to 0.69). 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 2013, there were more emergency care in resource-limited settings articles, while the number of disaster and humanitarian response articles decreased, when compared to the 2012 review. However, the distribution of articles selected for full review did not change significantly. As in prior years, the majority of articles focused on infectious diseases, as well as trauma and injury prevention.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.000 |
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; both teacher heads agree on what is shown here.
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".