The most cited works in epilepsy: Trends in the “Citation Classics”
Bibliographic record
Abstract
The number of times that a published article is cited is one indicator of its scientific impact. An article is termed a "Citation Classic" once it has accumulated more than 400 citations. Trends in these highly cited works allow projection of future directions of high-impact research within a field. Herein, we identified 89 articles in the field of epilepsy published in 35 different journals that have been cited more than 400 times (citation range 401-3,749). The journal that published the greatest number of Citation Classics was Epilepsia (9 articles with 656 mean citations per article). Laboratory studies constituted the fastest growing area of highly cited epilepsy research, whereas clinical studies showed a bimodal distribution in representation among Citation Classics. There were also considerably fewer epilepsy-specific Citation Classics compared to other disciplines. In this study, we find that the Citation Classics of epilepsy comprise a heterogeneous group of articles and that changes in the trends of these highly cited works represent the evolution of epilepsy research over time. The results of this study should inform the academic community and provide a guide of essential literature for scientists who are engaged in epilepsy research.
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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.034 | 0.051 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".