State anxiety predicts cognitive performance in patients with Parkinson’s disease.
Bibliographic record
Abstract
OBJECTIVE: Anxiety is common in Parkinson's disease (PD) and frequently a comorbidity that appears alongside nonmotor symptoms such as cognitive deficits; however, the relationship between anxiety and cognition in PD remains poorly understood. The aim of this study was to investigate the relationship between anxiety and specific cognitive domains (e.g., attention/working memory, executive functions, memory, language, and visuospatial function). METHOD: A total of 48 individuals with PD and 18 healthy controls were assessed using the State Trait Anxiety Inventory along with a comprehensive neuropsychological battery. Hierarchical multiple regression analysis was used to examine whether trait and/or state anxiety predicted deficits in overall cognitive function (Montreal Cognitive Assessment) and/or specific individual cognitive domains in the PD and healthy control samples while controlling for covariates such as age, depression (Geriatric Depression Scale), and Unified Parkinson's disease Rating Scale motor-subsection-III (PD only). RESULTS: Results showed that state anxiety in PD significantly predicted performance across an array of cognitive domains, such as attention/working memory, executive functioning, memory, and language, whereas trait anxiety was a predictor only for executive functioning. In contrast, there was no significant relationship between state anxiety and visuospatial ability Conclusions: Overall, these findings highlight that performance in particular cognitive domains are associated with anxiety in PD. Thus, it may be critically important to consider and quantify the contribution of anxiety to cognitive performance when diagnosing and treating dementia and/or mild cognitive impairments in PD. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".