Analysis of research trends and hotspots in emergency department overcrowding: A bibliometric study based on VOSview and Scimago Graphica
Bibliographic record
Abstract
ObjectiveAnalyze the research trends and hotspots in emergency department overcrowding derived from the Web of Science Core Collection database.MethodsThe Web of Science Core Collection database was utilized as the search data source for the bibliometric analysis, and the associated articles published from January 1, 1990, to October 1, 2023.The search was executed using the following formula: TS = (crowded OR overcrowd OR crowding OR overcrowding) AND TS = (Emergency department). VOSviewer, Scimago Graphicaand and additional tools were utilized for bibliometric analysis, and visual knowledge graphs were created.ResultsA total of 1869 articles were included in this study. The country with the largest number of publications is the United States. The primary research institution is the University of Toronto. Jesse M. Pines and his group at George Washington University have the greatest influence in the field of emergency department overcrowding research. Carlos A. Camargo is the author with the highest h-index in this field. High-frequency keywords include "length-of-stay", "impact", "mortality", "triage", "association", "outcomes", "time", "management", "access block", and "quality". The clustering graph reveals that all keywords fall into seven categories.ConclusionWe recommend intensifying research on emergency department overcrowding in more developing countries. In the future, the application of emerging technologies in emergency medicine as well as the mental health of emergency patients and medical staff may become research hotspots in this field.
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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.234 | 0.258 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".