Predicting treatment‐seeking for visual hallucinations among <scp>P</scp>arkinson's disease patients
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".