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Record W2418379189 · doi:10.1097/wco.0b013e328363304c

Affective disorders in Parkinsonʼs disease

2013· article· en· W2418379189 on OpenAlexafffund
Kelly Aminian, Antonio P. Strafella

Bibliographic record

VenueCurrent Opinion in Neurology · 2013
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental HealthToronto Western Hospital
FundersCanadian Institutes of Health ResearchCanada Research Chairs
KeywordsNeuroimagingParkinson's diseaseDiseaseDopaminergicNeurosciencePsychologyMedicineDopaminergic pathwaysDopaminePathology

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This review explores recent literature pertaining to affective disorders associated with Parkinson's disease. RECENT FINDINGS: Nonmotor symptoms including affective disorders are becoming more widely recognized as complications of Parkinson's disease. As awareness of these symptoms increases, and new neuroimaging tools are developed and become more accessible, more studies are being conducted pertaining to behavioral complications in Parkinson's disease. The functional connectivity of the basal ganglia can predispose people with Parkinson's to develop affective disorders. Furthermore, dopaminergic treatments may exacerbate or trigger behavioral symptoms. It is now understood that changes associated with Parkinson's disease are widespread, affecting striatal and extrastriatal regions and resulting in alterations in gray matter, white matter, blood flow, metabolism, and dopaminergic and serotonergic function. SUMMARY: Neuroimaging is advancing our knowledge of the mechanisms involved in Parkinson's disease, and their role in the development of behavioral disorders. An increased understanding of these disorders may lead to the discovery of new therapeutic targets, or the identification of risk factors for the development of these disorders. If preventive therapies become available, identification of risk factors will be important for the identification and treatment of susceptible individuals.

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 score0.036
Threshold uncertainty score0.658

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 head0.025
GPT teacher head0.313
Teacher spread0.288 · 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

Citations14
Published2013
Admission routes2
Has abstractyes

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