Bibliographic record
Abstract
Emergency medicine is a popular and new medical sub-specialty that provides rapid management of acute critical illness and injuries. In this study, it is aimed to perform bibliometric analysis of the articles about Emergency Medicine using the scientific mapping method. The Web of Science Core Collection (WoS) database was examined within the scope of this study and 3595 articles matching the search criteria were included in the research. For the analysis, the "bibliometrix 3.0" program which was developed in the R environment and "biblioshiny" web interface provider, which was developed for the use and visualization of this program, were used. It was found that the first article in the field of emergency medicine was published in 1980. It was also found that there was a significant increase in the number of articles after 1995 and peaked in 2021. Academic Emergency Medicine, Annals of Emergency Medicine and Journal of Emergency Medicine are the most influential journals in terms of publication and citation numbers and indexes. Professor Michelle Lin from the University of California, Professor Wendy C Coates from the UCLA Geffen School of Medicine, and Professor Gregory Luke Larkin from the Yale University School of Medicine are the most influential researchers in this field. Emory University, Michigan University, Brown University, which are American universities, are the most competent institutions in the field of emergency medicine. "Education" and "medical education" keywords are the most frequently used words along with emergency medicine. Recently “leadership”, “internsip” and “malpractice” issues are beginning to emerge. USA, Canada and United Kingdom are the leading countries in total number of publications, single-country and multi-country publications. It is considered that the research is original and its results will contribute to the relevant researchers about the publications in the field of emergency medicine.
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 | 0.012 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.152 | 0.186 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".