Association between cognitive and autonomic dysfunctions in patients with de novo Parkinson’s disease
Bibliographic record
Abstract
Cognitive decline is a common issue in Parkinson's disease (PD) and significantly affects patients' quality of life. This study explored the relationship between cognitive functions and dysautonomia in de novo PD. We reviewed records of newly diagnosed PD patients from July 2017 to September 2023 who underwent cognitive and autonomic assessments. Cognitive functions were measured using the Korean version of the Montreal Cognitive Assessment (MoCA-K) and the Seoul Neuropsychological Screening Battery, while autonomic functions were evaluated with the SCOPA-AUT questionnaire. Among 155 patients, 82 with de novo PD were included. The mild cognitive impairment (MCI) group exhibited higher SCOPA-AUT scores, particularly in gastrointestinal dysfunction. Multivariable logistic regression identified total SCOPA-AUT scores as significant predictors of MCI, even after adjusting for demographic and clinical factors. Partial correlation analysis showed significant negative associations between SCOPA-AUT scores and cognitive functions, such as memory and executive function. This study highlights a strong link between autonomic dysfunction, including gastrointestinal issues, and cognitive impairment in de novo PD. Monitoring dysautonomia in early-stage PD may aid in identifying patients at risk of cognitive decline.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".