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Record W2522809153

Pharmacogenetics of Non-Motor Symptoms in Parkinson's Disease

2016· article· en· W2522809153 on OpenAlexfundno aff
Brian D. Robertson

Bibliographic record

VenueScholarship@Western (Western University) · 2016
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsnot available
FundersCanada Excellence Research Chairs, Government of CanadaAcademic Medical Organization of Southwestern Ontario
KeywordsPharmacogeneticsParkinson's diseaseMedicineDiseasePhysical medicine and rehabilitationGenotypeGeneticsInternal medicineBiologyGene
DOInot available

Abstract

fetched live from OpenAlex

Memory deficits are recognized in Parkinson’s disease (PD). The nature of these memory deficits is unclear because few studies have both isolated memory encoding and retrieval processes while testing patients on and off their dopamine replacement medication. Previous work suggests encoding depends upon regions innervated by the ventral tegmental area, which is relatively spared in PD, while retrieval depends upon dorsal striatum, which is dopamine deficient even early in PD. We investigated the impact of a dopamine transporter (DAT1), a dopamine reuptake protein, polymorphism (a 40-base-pair variable repeat affecting expression) on encoding and retrieval in healthy, elderly controls as well as in patients on and off medication. We only found encoding deficits in PD patients who carry a DAT1 polymorphism when on, relative to off, medication, suggesting interactive effects of medication and genotype. We found improvements in memory retrieval in patients who were on, relative to off, medication, but this effect may be independent of DAT1 genotype. This work demonstrates the need for further investigation of interactive effects of medication and genetic profile 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.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.057
GPT teacher head0.318
Teacher spread0.261 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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