Association of motoric cognitive risk syndrome with depression in older adults: a meta-analysis and systematic review of cross-sectional and cohort studies
Bibliographic record
Abstract
In an aging society, depression has become a public health challenge that can lead to many adverse health outcomes; evidence addressing the link between depression and motor cognitive risk syndrome (MCR, a novel syndrome that effectively predicts dementia) is still lacking. A PRISMA checklist was used to systematically review the relevant peer-reviewed literature for the primary data analysis. A computer search of CNKI, Wan Fang Data, CBM, VIP, PubMed, Web of Science, Embase, Cochrane Library, Scopus, and Ovid databases, all from creation to March 15, 2023. A meta-analysis was conducted using Stata 17.0, the Newcastle-Ottawa Scale instrument (NOS), and the Agency for Healthcare Research and Quality (AHRQ) for quality appraisal. We used Q and I2 statistics to assess heterogeneity and random effects models to pool estimates. Egger’s regression tests and adjustments were made using the trim and fill test. Thirteen studies were included, including seven cross-sectional studies and six cohort studies, and data on the association between depression and MCR in the elderly were extracted. Moreover, the results of the meta-analysis showed that there was a significant correlation between depression and MCR in the cross-sectional study [OR = 2.43, 95% CI(1.31 ~ 3.54), P < 0.01]. In the cohort study, depression in the elderly was associated with the occurrence of MCR [HR = 1.19, 95% CI(1.08 ~ 1.30), P < 0.01]; Subgroup analysis by age, depression assessment tool, MCR assessment tool, and follow-up time (cohort study only) showed statistically significant differences (P < 0.01). Meta-analysis of cross-sectional studies and cohort studies showed that depression in older adults was associated with the development of MCR, and depression increased the risk of MCR in older adults.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".