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Record W4368360153 · doi:10.2217/nmt-2022-0039

5-HT <sub>1A</sub> Agonists for levodopa-induced Dyskinesia in Parkinson’s Disease

2023· review· en· W4368360153 on OpenAlex
Jawad Al‐Kassmy, Christine Sun, Philippe Huot

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeurodegenerative Disease Management · 2023
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsMcGill UniversityMcGill University Health CentreMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsDyskinesiaLevodopaMedicineParkinson's diseaseDiseaseAdverse effectClinical trialPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

Levodopa is the most effective agent for treating the symptoms of Parkinson’s disease (PD). However, levodopa-induced dyskinesia remains a significant complication that manifests after few years of treatment, for which therapeutic options remain limited. Several agonists of the serotonin type 1A (5-HT1A) receptor with varying levels of efficacy and interaction at other sites, have been tested in the clinic. Clinical trials testing 5-HT1A agonists have yielded inconsistent results in alleviating dyskinesia, especially that the antidyskinetic benefit observed was often accompanied by an adverse effect on motor function. In this article, we summarize and analyze the various clinical trials performed with 5-HT1A agonists in PD patients with dyskinesia and offer perspectives on the future of this class of agents in PD.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.950
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.080
GPT teacher head0.343
Teacher spread0.263 · 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