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Record W4408301578 · doi:10.1002/mds.30162

Update on Treatments for Parkinson's Disease Motor Fluctuations – An International Parkinson and Movement Disorder Society Evidence‐Based Medicine Review

2025· review· en· W4408301578 on OpenAlexafffund
Rob M.A. de Bie, Regina Katzenschlager, Bart Swinnen, Marina Peball, Shen‐Yang Lim, Tiago Mestre, Santiago Perez‐Lloret, Miguel Coelho, Camila Aquino, Ai Huey Tan, Verónica Bruno, Joke M. Dijk, Beatrice Heim, Chin‐Hsien Lin, Linda Azevedo Kauppila, Irene Litvan, René Spijker, Klaus Seppi, João Borges‐Costa, Cristina Sampaio, Susan H. Fox, Monty Silverdale

Bibliographic record

VenueMovement Disorders · 2025
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsUniversity of TorontoUniversity of CalgaryOttawa HospitalToronto Western HospitalUniversity of Ottawa
FundersDystonia CoalitionParkinsonfondenNational Institutes of HealthAOP OrphanCanadian Institutes of Health ResearchIpsenH. Lundbeck A/SStichting ParkinsonFondsUniversity of California, San DiegoUniversity of TorontoParkinson CanadaGW PharmaceuticalsMichael J. Fox Foundation for Parkinson's ResearchBoston Scientific CorporationAllerganInternational Parkinson and Movement Disorder SocietyPfizerBiogenParkinson's FoundationNovartisCurePSPMedical Research CouncilTeva Pharmaceutical IndustriesFondation Brain CanadaEisaiUniversity of OxfordParkinson's UKCHDI FoundationAustrian Science FundAmsterdam NeuroscienceZonMw
KeywordsRopiniroleLevodopaPramipexoleEntacaponeRotigotineMedicineParkinson's diseasePallidotomyRandomized controlled trialDyskinesiaDeep brain stimulationMovement disordersPhysical medicine and rehabilitationInternal medicineDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: To update evidence-based medicine recommendations for treating motor fluctuations of Parkinson's disease (PD). BACKGROUND: The International Parkinson and Movement Disorder Society (MDS) Evidence Based Medicine in Movement Disorders Committee recommendations for the treatments of PD were first published in 2002 and regularly updated. The current review uses a new methodology, including the Cochrane Risk of Bias tool and a modified version of GRADE (Grading of Recommendations, Assessment, Development, and Evaluations). METHODS: On January 1, 2023, a literature search was conducted without date limit in the MEDLINE, Embase, and Cochrane databases using the following search terms: Parkinson disease, levodopa and, for the Embase database, randomized controlled trial (RCT). The inclusion criteria for studies were: patients with PD, on oral levodopa therapy, experiencing motor fluctuations, investigating an intervention that was (commercially) available in at least one country, study design RCT, and with a follow-up duration of at least 3 months. RESULTS: A total of 102 studies were included. Levodopa extended release, pramipexole immediate release and extended release, ropinirole immediate release, rotigotine, opicapone, safinamide, and bilateral subthalamic nucleus deep brain stimulation (DBS) were assessed as efficacious, and continuous intestinal levodopa infusion, continuous subcutaneous levodopa, continuous subcutaneous apomorphine, ropinirole prolonged release, ropinirole patch, entacapone, rasagiline, istradefylline, amantadine extended release, zonisamide, bilateral globus pallidus DBS, and pallidotomy were assessed as likely efficacious for the treatment of motor fluctuations in people with PD who are already being treated with levodopa. CONCLUSIONS: There are several treatment options that can improve motor fluctuations in PD. These recommendations will assist physicians and patients in determining which intervention to use. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0090.008
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.064
GPT teacher head0.382
Teacher spread0.318 · 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 designSystematic review
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

Citations48
Published2025
Admission routes2
Has abstractyes

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