Association of Essential Tremor With Dementia and Affective Disorders: A Meta-Analysis
Bibliographic record
Abstract
Background The dementia and affective disorders are common non-motor features in patients with essential tremor (ET). However, the relationship of ET with cognitive impairments and affective disorders remains controversial. This meta-analysis aimed to analyze the association of ET with dementia and affective disorders. Methods Original studies published from January 1999 to October 2019 were systematically searched from the database of Medline (OvidSP), EMBASE (OvidSP), and the Cochrane Central Register of Controlled Trials. Pooled standard mean difference (SMD, random effect model), odds ratios (ORs), relative risk (RR), and 95% CI were calculated. Results Compared with the Non-ET group, patients with ET had significantly lower Mini-Mental State Examination (MMSE) score (SMD, −1.16; 95% CI, −1.75 to −0.58; p = 0.0001) and had significantly higher depressive and anxiety symptoms scale score (SMD, 0.55; 95% CI, 0.22–0.87; p = 0.0009). The OR for dementia and affective disorders in individuals with ET compared with individuals without ET was 2.49 (95% CI, 2.17–2.85, p < 0.00001). While there was no significant difference in Montreal Cognitive Assessment (MoCA) score between ET and Non-ET groups (SMD, −0.52; 95% CI, −0.16 to 0.13; p = 0.23), there was a significant difference in the risk of mortality between ET and Non-ET groups (RR = 4.69, 95% CI, 2.18–10.07). Conclusion The non-motor symptoms should not be neglected among patients with ET. However, the causal relationship between ET and dementia, depression, and anxiety is unclear.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.045 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".