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Record W1606794225 · doi:10.1111/pcn.12083

Predicting treatment‐seeking for visual hallucinations among <scp>P</scp>arkinson's disease patients

2013· article· en· W1606794225 on OpenAlexaff
Abdul Qayyum Rana, Ishraq Siddiqui, Masood Zangeneh, Abdul Fattah, Naeem Awan, Muhammad Saad Yousuf

Bibliographic record

VenuePsychiatry and Clinical Neurosciences · 2013
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of TorontoThe Scarborough HospitalParkinson's Clinic of Eastern Toronto & Movement Disorders Centre
Fundersnot available
KeywordsVisual HallucinationDiseaseParkinson's diseasePsychologyDementiaPerspective (graphical)PsychiatryClinical psychologyMedicineEmotional reactionInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

AIM: While much research has been conducted towards understanding the basis of visual hallucinations in Parkinson's disease, little has focused on characterizing the content and patients' emotional experience. These factors are likely very influential on a patient's decision to seek treatment, a critical aspect of any symptom from the clinical perspective. 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: The study consisted of 334 patients with Parkinson's disease, among whom 10.5% had visual hallucinations. Hoehn and Yahr disease stage (P = 0.001), concurrent presence of dementia (P = 0.001),and sex (P = 0.031) were significant onset predictors. The most significant determinant of treatment-seeking was emotional reaction, namely whether hallucinations were bothersome (P = 0.008). However, the specific type of content during hallucinations was sometimes more influential and contradicted emotional response. CONCLUSION: Although treatment-seeking can be predicted by how individuals feel about hallucinations, a patient's decision may not be logically consistent. We suggest that clinicians offer treatment based on patients' recollections and opinions.

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)0.001
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 score0.009
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.026
GPT teacher head0.331
Teacher spread0.305 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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