Knowledge Atlas of Insular Epilepsy: A Bibliometric Analysis
Bibliographic record
Abstract
Objective: In order to determine research hotspots and prospective directions, this work used VOSviewer and CiteSpace to assess the current state of insular epilepsy research. Methods: We looked for pertinent research about insular epilepsy published between the first of January 2000 and the thirtieth of April 2022 in the Web of Science Core Collection (WoSCC) database. CiteSpace and VOSviewer were used to build a knowledge atlas by analyzing authors, institutions, countries, keywords with citation bursts, keyword clustering, keyword co-occurrence, publishing journals, reference co-citation patterns, and other factors. Results: A total of 305 publications on insular epilepsy were found. Nguyen DK had the most articles published (37), whereas Mauguière F and Isnard J had the highest average number of citations/publications (39.37 and 38.09, respectively). The leading countries and institutions in this field were the United States (82 papers) and Université de Montréal (40 papers). Authors, countries, and institutions appear to be actively collaborating. Hot topics and research frontiers included surgical treatment, functional network connectivity, and the application of neuroimaging methods to study insular epilepsy. Conclusion: In summary, the most influential articles, authors, journals, organizations, and countries on the subject of insular epilepsy were determined by this analysis. This study investigated the area of insular epilepsy research and forecasted upcoming trends using co-occurrence and evolution methods.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.185 | 0.176 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".