Research hotspots and frontiers of alcohol and epilepsy: A bibliometric analysis
Bibliographic record
Abstract
PURPOSE: Alcohol is implicated in epileptogenesis and seizures attack. An increasing number of studies about alcohol and epilepsy have been published. We aimed to assess research trends and hot spots in the field of alcohol and epilepsy. PATIENTS AND METHODS: Literature concerning alcohol and epilepsy was systemically searched through the Web of Science database. Collaborative maps were quantitatively analyzed by using the VOSviewer and CiteSpace tools. RESULTS: A total of 1578 papers about the field of alcohol and epilepsy were taken into analysis, which was written by 6840 authors from 2153 institutions in 85 countries, published in 676 journals, and cited 79 667 references from 10 750 journals. The United States was the leading country and had close ties with others. The University of Toronto was the most productive institution. Alcoholism-clinical and experimental research was the fastest-growing journal. Richard J. Bodnar was the author contributing the most literature. Analysis of keywords showed epilepsy, alcohol, seizures, alcohol withdrawal, and management were common themes. CONCLUSION: The presented study conducted the first bibliometric analysis of the field of alcohol and epilepsy, which will provide insights into the latest progress, evolution paths, frontier research hot spots, and future research trends in the field.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.057 | 0.089 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".