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Record W2898096496 · doi:10.1002/ana.25364

Levodopa‐induced dyskinesia in Parkinson disease: Current and evolving concepts

2018· review· en· W2898096496 on OpenAlexaff
Alberto J. Espay, Francesca Morgante, Aristide Merola, Alfonso Fasano, Luca Marsili, Susan H. Fox, Erwan Bézard, Barbara Picconi, Paolo Calabresi, Anthony E. Lang

Bibliographic record

VenueAnnals of Neurology · 2018
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsOntario Brain InstituteToronto Western HospitalUniversity of TorontoUniversity Health Network
FundersAdamas Pharmaceuticals
KeywordsLevodopaDyskinesiaDopamineNeuroscienceParkinson's diseaseMedicineStimulationNicotinic agonistGlutamatergicSerotonergicPsychologyDiseaseInternal medicineReceptorSerotoninGlutamate receptor

Abstract

fetched live from OpenAlex

Levodopa-induced dyskinesia is a common complication in Parkinson disease. Pathogenic mechanisms include phasic stimulation of dopamine receptors, nonphysiological levodopa-to-dopamine conversion in serotonergic neurons, hyperactivity of corticostriatal glutamatergic transmission, and overstimulation of nicotinic acetylcholine receptors on dopamine-releasing axons. Delay in initiating levodopa is no longer recommended, as dyskinesia development is a function of disease duration rather than cumulative levodopa exposure. We review current and in-development treatments for peak-dose dyskinesia but suggest that improvements in levodopa delivery alone may reduce its future prevalence. Ann Neurol 2018;84:797-811.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.147
GPT teacher head0.422
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations340
Published2018
Admission routes1
Has abstractyes

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