The Most Cited Works in Essential Tremor and Dystonia
Bibliographic record
Abstract
Background: The study of the most cited works in a particular field gives an indication of the important advances, developments, and discoveries that have had the highest impact in that discipline. Our aim was to identify the most cited works in essential tremor (ET) and dystonia. Methods: A bibliometric search was performed using the ISI Web of Science database using selected search terms for ET and dystonia for articles published from 1900 to 2015. The resulting citation counts were analyzed to identify the most cited works, and the studies were categorized. Results: Using the criterion of more than 400 citations, there were four citation classics for ET and six for dystonia. The most cited studies were those on pathophysiology followed by medical treatments, clinical classification, genetic studies, surgical treatments, review articles, and epidemiology studies. A comparison of the most cited articles for ET and dystonia showed that there was a divergence, with ET and dystonia having a higher number of epidemiologic and genetic studies, respectively. Whereas the peak period for the number of publications was 2000–2004 for ET, it was 1995–1999 for dystonia. Discussion: Given the large number of patients with these disorders, there appears to be an unmet need for further research advances in both areas, but particularly for ET as the most common movement disorder.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.106 | 0.129 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".