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Record W4253880639 · doi:10.5334/tohm.283

The Most Cited Works in Essential Tremor and Dystonia

2016· article· en· W4253880639 on OpenAlexaff
Nicolas Kon Kam King, Joseph Tam, Alfonso Fasano, Andrés M. Lozano

Bibliographic record

VenueTremor and Other Hyperkinetic Movements · 2016
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsToronto Western HospitalCentre for Movement DisordersUniversity of Toronto
Fundersnot available
KeywordsDystoniaCitationWeb of scienceEpidemiologyBibliometricsMedicinePsychologyPsychiatryLibrary sciencePathologyComputer scienceMeta-analysis

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.302

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.250
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2016
Admission routes1
Has abstractyes

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