The heterogeneity of cognitive symptoms in Parkinson's disease: a meta-analysis
Bibliographic record
Abstract
Several studies have reported heterogeneity in cognitive symptoms associated with specific characteristics of patients with Parkinson's disease (PD). Indeed, researchers have characterised subtypes of patients suffering from PD according to various criteria. Those most frequently used are the type of predominant motor symptoms (tremors or non-tremor symptoms), age at onset and presence of depression. Some characteristics, like the predominant motor subtypes, as well as the presence of depression, are more widely used to categorise cognitive differences between patients. The goal of this study was to analyse the impact of the type of predominant motor symptoms and depression on cognition in PD. A meta-analysis of 27 studies (from 1989 to 2012) was carried out to calculate the average effect size of these factors on the most often used cognitive test during those past years to evaluate cognitive skills, the Mini-Mental State Examination. The studies analysed showed significant mean weighted effect sizes on cognition for the type of motor symptoms (d=0.42; 95% CI 0.30 to 0.54) and for depression (d=0.52; 95% CI 0.38 to 0.66). These results suggested that PD participants with non-tremor predominant motor symptoms or with depression had more or more severe cognitive impairments. Identification of different subtypes in PD is important for a better understanding of the cognitive symptoms associated with this disease. Better knowing the impact of different features of PD subgroups could help to design more appropriate treatments for patients with PD.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.070 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".