Impact of Obstructive Sleep Apnea on Cognitive and Motor Functions in Parkinson's Disease
Bibliographic record
Abstract
INTRODUCTION: Parkinson's disease (PD) is a chronic neurodegenerative disorder that presents with motor and non-motor manifestations. Amongst the non-motor features, various forms of sleep disturbances can occur, and obstructive sleep apnea (OSA) is considered to be a common comorbidity. We conducted this systematic review and meta-analysis to assess the impact of OSA on cognitive and motor functions in PD. METHODS: The information sources of for this systematic review and meta-analysis were PubMed, SCOPUS, Web of Science, and ScienceDirect. Studies meeting the following criteria were included: (1) studies including idiopathic PD patients, (2) studies using polysomnography to categorize PD patients into PD with OSA and PD without OSA, and (3) studies with observational designs (case-control, cohort, or cross-sectional). Data analysis was performed using RevMan. RESULTS: Our meta-analysis showed that OSA was associated with significantly lower scores of Montreal Cognitive Assessments (MoCA) (mean difference (MD) = -0.70, 95% confidence interval (CI) [-1.28, -0.13], P = 0.01) and Mini-Mental State Examination (MMSE) (MD = -0.69, 95% CI [-1.17, -0.21], P = 0.005). Moreover, the score of the motor part of the Unified Parkinson's Disease Rating Scale (UPDRS III) was significantly higher in PD patients with OSA as compared with those without OSA (MD = 1.63, 95% CI [0.03, 3.23], P = 0.049). CONCLUSIONS: OSA is associated with increased severity of PD-associated cognitive dysfunction and motor symptoms. However, further studies are needed to corroborate these findings, assess the underlying mechanisms by which OSA influences the motor and cognitive functions in PD, and investigate whether OSA can accelerate the neurodegenerative process of PD. © 2020 International Parkinson and Movement Disorder Society.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.018 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".