Pediatric Epilepsy Surgery: Bibliometric Analysis to Date
Bibliographic record
Abstract
INTRODUCTION: Pediatric epilepsy surgery has significantly evolved since the very first epilepsy procedure that was performed by Victor Horsely in 1886. Bibliometrics analysis serves as an objective tool for the assessment of the scientific literature, research trends, and innovations over the years. The aim of this study is to provide a bibliometric analysis of all the studies published on pediatric epilepsy surgery to date. METHODS: All relevant published and indexed articles pertaining to pediatric epilepsy surgery were captured through a comprehensive literature search of the Scopus database from January 1, 1953, through to February 1, 2022. A validated set of bibliometrics parameters were extracted and analyzed. All analyses were performed on R 4.1.2. RESULTS: A total of 4007 articles were published between 1953 and 2021. There was an annual publication growth rate of 14.37% per year published in more than 525 sources (Figure). The institutions that contributed the largest number of publications were Wayne State University (n = 300), the University of California (n = 243), and the University of Toronto (n = 157). Further, the countries that received the most citations were the US (n = 46613), Germany (n = 13861), and Canada (n = 8516). Each article had 28.4 citations and included 6.7 authors per paper.FigureCONCLUSION: This study included a comprehensive scientometric analysis to understand the evolution of research trends over time. This data can be utilized to develop a standard of care while also understanding the changes of knowledge over time.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.142 | 0.234 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".