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Record W1584748823 · doi:10.1002/mds.26121

Behavioral effects of levodopa

2014· article· en· W1584748823 on OpenAlexaff
Isabelle Beaulieu‐Boire, Anthony E. Lang

Bibliographic record

VenueMovement Disorders · 2014
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsToronto Western HospitalUniversité de Sherbrooke
Fundersnot available
KeywordsLevodopaHypomaniaManiaDopaminergicParkinsonismPsychologyApathyDeep brain stimulationPsychosisHypersexualityDopaminergic pathwaysPsychiatryNeuroscienceImpulse control disorderMedicineDopamineDiseasePathologicalBipolar disorderParkinson's diseaseCognitionInternal medicine

Abstract

fetched live from OpenAlex

In recent years, levodopa-induced behavioral changes have received increased attention in the medical literature and in clinical practice. The spectrum of these symptoms includes non-motor fluctuations with neuropsychiatric symptoms, compulsive behaviors such as punding, dopamine dysregulation syndrome, and impulse control disorders, psychosis and hallucinations, as well as hypomania and mania. Despite knowledge of the clinical features associated with these behaviors, many of them are probably underdiagnosed. Although the mechanisms underlying behavioral symptoms are still incompletely understood, recent data from imaging and pathological studies have increased our understanding and guided new treatment strategies. Appropriate management remains challenging, because reduction of levodopa (l-dopa) and dopaminergic treatment is often recommended; however, doses required for control of motor symptoms may still induce behavioral changes. Newer modes of delivery of dopaminergic treatment, deep brain stimulation, and nondopaminergic agents may either provide alternatives for treatment of these behavioral problems or permit treatment of parkinsonism with less risk of these behavioral disorders.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.008
GPT teacher head0.258
Teacher spread0.250 · 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

Citations86
Published2014
Admission routes1
Has abstractyes

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