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Prevalence and relation of dementia to various factors in Parkinson's disease

2012· article· en· W1505429348 on OpenAlexaff
Abdul Qayyum Rana, Muhammad Saad Yousuf, Sughra Naz, Nour Qa’aty

Bibliographic record

VenuePsychiatry and Clinical Neurosciences · 2012
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsParkinson's Clinic of Eastern Toronto & Movement Disorders Centre
Fundersnot available
KeywordsDementiaParkinson's diseaseDiseaseMedicineDegenerative diseasePsychologyPsychiatryPediatricsInternal medicine

Abstract

fetched live from OpenAlex

AIMS: Parkinson's disease is a chronic neurodegenerative disorder characterized by bradykinesia, rigidity, and resting tremor. Dementia, among its non-motor symptoms, is a debilitating complication affecting intellectual functioning. The aim of the present study was to determine the prevalence of dementia in Parkinson's disease and its relation to age, gender and stage of the disease. METHODS: A retrospective chart analysis was performed on Parkinson's disease patients seen in a community-based Parkinson's disease and movement disorder clinic between 2005 and 2010. RESULTS: A total of 310 patients were included in this survey, among whom 61 patients (19.7%) with Parkinson's disease met the criteria for dementia. Age was found to be a significant factor in developing dementia, with 90% of patients with dementia aged ≥70. Gender, however, was not correlated with dementia in Parkinson's disease. On analysis of stage at which dementia developed, progression of the disease was positively correlated with prevalence of dementia. CONCLUSIONS: As age increases, the chances of developing dementia increase. Dementia, contrarily, is not selective between genders. The likelihood of developing dementia increases as the stage of disease advances. Further research is required in order to understand underlying mechanisms of dementia in Parkinson's disease.

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 score0.001
Threshold uncertainty score0.271

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 head0.034
GPT teacher head0.337
Teacher spread0.303 · 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

Citations21
Published2012
Admission routes1
Has abstractyes

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