Global Emergency Medicine: A Review of the Literature From 2016
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 13,890 articles written in four 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. All articles that were deemed appropriate by at least one reviewer and approved by their editor underwent formal scoring of overall quality and importance. Two independent reviewers scored all articles. RESULTS: A total of 716 articles met our inclusion criteria and underwent full review. Fifty-nine percent were categorized as emergency care in resource-limited settings, 17% as EM development, and 24% as disaster and humanitarian response. Nineteen articles received scores of 18.5 or higher out of a maximum score of 20 and were selected for formal summary and critique. Inter-rater reliability testing between reviewers revealed Cohen's kappa of 0.441. CONCLUSIONS: In 2016, the total number of articles identified by our search continued to increase. The proportion of articles in each of the three categories remained stable. Studies and reviews with a focus on infectious diseases, pediatrics, and the use of ultrasound in resource-limited settings represented the majority of articles selected for final review.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.038 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| 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".