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Record W2052119205 · doi:10.1002/mds.23983

Perampanel, an AMPA antagonist, found to have no benefit in reducing “off” time in Parkinson's disease

2011· article· en· W2052119205 on OpenAlexaff
Andrew J. Lees, Stanley Fahn, Karla Eggert, Joseph Jankovic, Anthony E. Lang, Federico Micheli, M. Maral Mouradian, Wolfgang H. Oertel, C. Warren Olanow, Werner Poewe, Olivier Rascol, Eduardo Tolosa, David Squillacote, Dinesh Kumar

Bibliographic record

VenueMovement Disorders · 2011
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsPerampanelPlaceboAntagonistDyskinesiaLevodopaAnesthesiaPsychologyMedicinePharmacologyParkinson's diseaseInternal medicineAdverse effectDiseaseReceptor

Abstract

fetched live from OpenAlex

BACKGROUND: Perampanel is a selective, noncompetitive α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor antagonist. Two multicenter randomized, double-blind, placebo-controlled, parallel-group phase III studies assessed the efficacy and safety of adjunctive perampanel in patients with Parkinson's disease and motor fluctuations. METHODS: In both phase III studies (301 and 302), levodopa-treated patients were randomized and treated with once-daily oral placebo (n = 504), perampanel 2 mg (n = 509), or perampanel 4 mg (n = 501). The primary end point was change in daily "off" time from baseline. The treatment period was 30 weeks in study 301 and 20 weeks in study 302. RESULTS: For any efficacy end point, perampanel 2 or 4 mg was not superior to placebo. Perampanel was well tolerated up to 4 mg/day. CONCLUSIONS: Perampanel failed to significantly improve motor symptoms versus placebo. There was also no effect on the duration or disability of levodopa-induced dyskinesia.

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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.260
Teacher spread0.239 · 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 designNon-randomized trial
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

Citations77
Published2011
Admission routes1
Has abstractyes

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