Global Emergency Medicine: A Review of the Literature From 2012
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, our search identified 4,818 articles written in six languages. These articles were distributed among 20 reviewers for initial screening based on their relevance to the field of global EM. Two additional reviewers searched and screened the grey literature. A total of 224 articles were deemed appropriate by at least one reviewer and were approved by their editor for formal scoring of overall quality and importance. RESULTS: Of the 224 articles that met our predetermined inclusion criteria, 56% were categorized as Emergency Care in Resource-limited Settings, 18% as EM development, and 26% as Disaster and Humanitarian Response. A total of 28 articles received scores of 16 or higher and were selected for formal summary and critique. Inter-rater reliability for two reviewers using our scoring system was good, with an intraclass correlation coefficient of 0.625 (95% confidence interval = 0.512 to 0.711). CONCLUSIONS: In 2012 there were more disaster and humanitarian response articles than in previous years. As in prior years, the majority of articles addressed the acute management of infectious diseases or the care of vulnerable populations such as children and pregnant women.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.041 | 0.034 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".