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Record W4301326879 · doi:10.33590/emj/10310305

Clinical Management of Drug-Induced Dyskinesia in Parkinson’s Disease: Why Current Approaches May Need to Be Changed to Optimise Quality of Life

2016· article· en· W4301326879 on OpenAlexfundno aff
Jean‐François Daneault, Gloria Vergara-Díaz, Sunghoon Ivan Lee

Bibliographic record

VenueEuropean Medical Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchFundación Alfonso Martín EscuderoMichael J. Fox Foundation for Parkinson's Research
KeywordsDyskinesiaQuality of life (healthcare)DiseaseDrugMedicineParkinson's diseaseCurrent (fluid)Intensive care medicinePsychologyPharmacologyInternal medicineEngineering

Abstract

fetched live from OpenAlex

Parkinson’s disease is a complex, progressive neurodegenerative disorder associated with both motor and non-motor symptoms. Current treatment strategies mainly target the alleviation of motor symptoms through dopaminergic replacement therapy. Many patients with Parkinson’s disease will eventually experience motor complications associated with their anti-parkinsonian medication. One of those complications is drug-induced dyskinesia. This paper firstly reviews current approaches to the management of drug-induced dyskinesia, from modifications to the titration of medication, to more invasive approaches like deep brain stimulation. Following this we describe a recent proposal suggesting that the treatment of dyskinesia should be based on the impact on daily activities of patients rather than on the mere presence of the condition. Next, we discuss how this approach could improve the quality of life of patients and their caregivers and finally, we suggest possible ways of implementing this approach in practice.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 integrity0.0000.000
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 head0.160
GPT teacher head0.374
Teacher spread0.214 · 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 designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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