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Record W4389150711 · doi:10.1136/jnnp-2023-abn.115

Sex differences in cognitive function in Parkinson’s disease

2023· article· en· W4389150711 on OpenAlexaboutno aff
Camboe Ellen, Markovic-Obiago Zara, Zirra Alexandra, Tahrina Haque, Dey Kamalesh, Ben-Joseph Aaron, Gallagher David, Badu Caroline, Marshall Charles, Noyce Alastair

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldNeuroscience
TopicNuclear Receptors and Signaling
Canadian institutionsnot available
Fundersnot available
KeywordsCognitionDiseaseFunction (biology)Parkinson's diseaseComputer sciencePsychologyMedicineNeuroscienceBiology

Abstract

fetched live from OpenAlex

<h3>Aim</h3> To determine sex differences in cognitive function in patients with Parkinson’s disease (PD) in East London. <h3>Background</h3> Sex influences the development and progression of PD. Previous studies have identified higher rates, and faster progression, of cognitive impairment in men. Although this raises important consi- derations for clinical management, it has been understudied in diverse populations. <h3>Methods</h3> We included 184 PD patients enrolled in the East London Parkinson’s disease project (113 male and 71 female). The primary outcome for this analysis was the Montreal Cognitive Assessment (MoCA). <h3>Results</h3> Men were younger at assessment (mean 67.0 vs 70.4, p=0.042) and women left education earlier (mean 17.2 vs 19.5, p=0.009). Both sexes were below the UK average for deprivation index, but there was no difference between them (mean 4.2 male vs 3.6 female, p=0.23). Overall, there was no difference in cognitive impairment as assessed by MoCA (mean total men 21.7 vs women 20.8, p=0.539). Within ethnic groups, there were also no sex differences evident (White p=0.388, Black p=0.612, South Asian p=0.256). <h3>Conclusions</h3> Although men are considered as having higher cognitive impairment burden, we propose that in diverse populations, other drivers such as education level and deprivation may be stronger deter- minants than sex.

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 score0.034
Threshold uncertainty score0.643

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)0.0010.000

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 head0.063
GPT teacher head0.275
Teacher spread0.212 · 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
Published2023
Admission routes1
Has abstractyes

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