MétaCan
Menu
Back to cohort
Record W2046293951 · doi:10.2147/nedt.2007.3.1.1

Pathological gambling and dopamine agonists: A phenotype?

2007· article· en· W2046293951 on OpenAlexaboutno aff
Roger M. Pinder

Bibliographic record

VenueNeuropsychiatric Disease and Treatment · 2007
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDopaminePhenotypePathologicalNeuroscienceBioinformaticsPharmacologyInternal medicineGeneticsGene

Abstract

fetched live from OpenAlex

Pathological gambling and dopamine agonists: A phenotype?Therapeutic dopamine agonists have been around a long time ever since the defi cit of brain dopamine in Parkinson's disease (PD) was fi rst reported in 1960 (Ehringer and Hornykiewicz 1960) swiftly followed by the fi rst clinical trial of levodopa administration in Parkinsonian patients (Birkmayer and Hornykiewicz 1961).Design of new dopamine agonists was also a fi rst love of the Editor of Neuropsychiatric Disease and Treatment (Pinder 1970;Miller et al 1974).In those fi rst heady years effi cacy in a previously untreatable but rather common neurological disorder seemed more important than side effects, especially as more selective agonists of dopamine than levodopa came on stream.Indeed, in addition to the many variations on levodopa, such as different pharmaceutical formulations and various combinations with enzyme inhibitors, there is now an armamentarium of such drugs available including ergoline derivatives like bromocriptine, cabergoline, and pergolide and non-ergolines such as pramipexole, ropinirole, and rotigotine.Dopamine agonists are even being used as monotherapy in early PD before levodopa-containing drugs are considered (Clarke and Guttman 2002).Cabergoline and pergolide have recently been associated with an increased risk of valvular heart disease in PD patients (Schade et al 2007;Zanettini et al 2007), because of these fi ndings, pergolide was recently withdrawn from the US market by the FDA.Among the many side effects of all dopamine agonists as treatments for PD are impulse control disorders such as pathological gambling (Driver-Dunckley et al 2003;Dodd et al 2005).Although serious, and often fi nancially and socially catastrophic for the individual patient, compulsive gambling is relatively uncommon and the predictive features for determining who is likely to experience impulsive behavior are unknown.However, in this issue, the group of Hubert Fernandez at the McKnight Brain Institute at the University of Florida proposes a possible 'phenotype' based on the four As: anxiety, anger, age, and agonists (Shapiro et al 2007).They have analyzed whether lifestyle or environmental factors are associated with pathological gambling in PD.Although the sample is relatively small, compulsive gamblers appear to be younger and exhibit higher levels of anxiety, anger, and confusion.A Canadian report has also been published in February with a somewhat larger sample, which confi rms the younger age of compulsive gamblers and the use of dopamine agonists, together with a history of medication-induced hypomania or mania, higher novelty seeking, and a personal or immediate family history of alcohol use disorders (Voon et al 2007).If these vulnerabilities can be confi rmed in larger and longer-term studies, particularly in other PD populations across the globe, then we may be able to talk about a true phenotype.Identifi cation of PD patients potentially at risk for developing compulsive gambling behavior would be particularly useful to clinicians prescribing dopamine agonists enabling them to be extra vigilant when dealing with this particular sub-population.Dopamine agonists are also used in another chronic neurological disorder, restless legs syndrome (RLS), despite the lack of a precise dopaminergic pathophysiology for the disorder (Happe and Trenkwalder 2004;Trenkwalder et al 2005).The last issue of Neuropsychiatric Disease and Treatment was largely devoted to RLS, and included major reviews on RLS-associated disturbances of sleep (Bogan 2006) and mood (Becker 2006), evaluations of pramipexole (Benbir and Guilleminault 2006),

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.023
GPT teacher head0.282
Teacher spread0.259 · 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 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

Citations9
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueNeuropsychiatric Disease and TreatmentSame topicParkinson's Disease Mechanisms and TreatmentsFrench-language works237,207