Current trends and hotspots in drug-resistant epilepsy research: Insights from a bibliometric analysis
Bibliographic record
Abstract
Background Drug-resistance is a significant clinical issue in persons with epilepsy. In the past few years, many studies have been published investigating the management of drug-resistant epilepsy (DRE); however, no systematic and quantitative evaluation of this research has been performed. Therefore, a bibliometric analysis was conducted to demonstrate the current status of DRE research and to reflect the trends and hotspots within the field. Methods We retrieved publications on DRE published between 2011 and 2021 from the Science Citation Index Expanded of the Web of Science Core Collection. All articles related to DRE were included in this study. VOSviewer, R software, and CiteSpace were used to perform bibliometric research. Results A total of 3,088 original articles were included in this study. The number of publications on DRE has continued to increase over the past 11 years. The USA published the most papers with the highest number of citations and H-index. The National Institutes of Health and the University of Toronto were the most prolific funding agency and affiliation, respectively. Epilepsy & Behavior and Epilepsia ranked first as the most prolific and co-cited journals, respectively. The keywords “cannabidiol”, “neuromodulation”, “seeg” and “perampanel” revealed recent research hotspots. The top 100 most cited papers were classified into eight main topics, of which pharmacotherapy, disease mechanisms/pathophysiology, and neuromodulation were the three most important topics. Conclusions This analysis of bibliometric data demonstrated that DRE has always been a topical area of research. The mechanisms of epilepsy and therapies have been the focus of DRE research, and innovative antiseizure medications and surgical approaches are fast-developing research trends.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.192 | 0.249 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".