MétaCan
Menu
Back to cohort
Record W2944266808 · doi:10.1016/j.nbd.2019.05.001

Dystonia and Parkinson's disease: What is the relationship?

2019· review· en· W2944266808 on OpenAlexaff
Aakash Shetty, Kailash P. Bhatia, Anthony E. Lang

Bibliographic record

VenueNeurobiology of Disease · 2019
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsDystoniaParkinsonismDeep brain stimulationLevodopaDopamineParkinson's diseaseNeuroscienceMovement disordersPallidotomyMedicinePsychologyDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Dystonia and Parkinson's disease are closely linked disorders sharing many pathophysiological overlaps. Dystonia can be seen in 30% or more of the patients suffering with PD and sometimes can precede the overt parkinsonism. The response of early dystonia to the introduction of dopamine replacement therapy (levodopa, dopamine agonists) is variable; dystonia commonly occurs in PD patients following levodopa initiation. Similarly, parkinsonism is commonly seen in patients with mutations in various DYT genes including those involved in the dopamine synthesis pathway. Pharmacological blockade of dopamine receptors can cause both tardive dystonia and parkinsonism and these movement disorders syndromes can occur in many other neurodegenerative, genetic, toxic and metabolic diseases. Pallidotomy in the past and currently deep brain stimulation largely involving the GPi are effective treatment options for both dystonia and parkinsonism. However, the physiological mechanisms underlying the response of these two different movement disorder syndromes are poorly understood. Interestingly, DBS for PD can cause dystonia such as blepharospasm and bilateral pallidal DBS for dystonia can result in features of parkinsonism. Advances in our understanding of these responses may provide better explanations for the relationship between dystonia and Parkinson's disease.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.882
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.062
GPT teacher head0.334
Teacher spread0.272 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations113
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueNeurobiology of DiseaseSame topicNeurological disorders and treatmentsFrench-language works237,207