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Record W1972680583 · doi:10.14740/jnr.v4i2-3.281

Premorbid Personality and the Risk of Parkinson’s Disease

2014· article· en· W1972680583 on OpenAlexvenueno aff
Kelly L. Sullivan, James A. Mortimer, Wei Wang, Theresa A. Zesiewicz, H. James Brownlee, Amy R. Borenstein

Bibliographic record

VenueJournal of Neurology Research · 2014
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPersonalityMedicineOdds ratioLogistic regressionDiseaseRecallConfidence intervalClinical psychologyBig Five personality traitsGerontologyDemographyPsychiatryPsychologyInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

Background: Previous studies support the hypothesis that premorbid personality characteristics may be associated with the risk of Parkinson’s disease (PD). However, most of these relied upon subjective reports of premorbid personality earlier in life, which may be subject to recall bias. The objective of the current study was to evaluate the association of PD with risk-taking, routinization, smoking and alcohol consumption in early-adult life as indicators of premorbid personality. Methods: In-person interviews were conducted with 89 PD patients and 99 controls from a university-based medical center. Associations between indicators of early-adult personality and risk of PD were examined using logistic regression. Results: Adjusting for age, sex and education, taking or wanting to take more activity risks as a young adult was inversely associated with the risk of PD in the entire sample (odds ratio (OR) = 0.78 (95% confidence interval (CI) 0.63 - 0.97)). Among women, higher levels of routinization as a young adult were associated with an increased risk of PD (OR = 1.63 (95% CI 1.05 - 2.53)). Conclusions: Parkinson patients were more likely to take or want to take fewer risks in early-adult life and to prefer a more routine lifestyle than controls, suggesting that individuals with PD may have distinctive premorbid personality characteristics.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.017
Threshold uncertainty score0.272

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.351
Teacher spread0.304 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

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