Repetitive negative thinking is associated with cognitive function decline in older adults: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Psychological problems such as depression and anxiety increase the risk of cognitive impairment in older adults. But mechanisms on the effect of psychological disorder on cognitive function is inconclusive. Repetitive negative thinking (RNT) is a core symptom of a number of common psychological disorders and may be a modifiable process shared by many psychological risk factors that contribute to the development of cognitive impairment. RNT may increase the risk of cognitive impairment. However, there are fewer studies related to RNT and cognitive function, and there is a lack of epidemiological studies to explore the relationship between RNT and cognitive function. METHODS: A cross-sectional study of 424 older adults aged 60 years or over was performed form May to November 2023 in hospital. To investigate the RNT level by using the Perseverative Thinking Questionnaire (PTQ), and investigate the cognitive function level by using the Montreal Cognitive Assessment Scale (MoCA). Multivariable linear regression and subgroup analyses were used to explore the relationship between RNT and cognitive function. RESULTS: We categorized the total RNT scores into quartiles. The multivariable linear regression analysis showed that after adjusting for all covariates, the participants in the Q3 and Q4 groups exhibited lower cognition scores (Q3:β = -0.180, 95%CI -2.849~-0.860; Q4:β = -0.164, 95% -2.611~-0.666) compared to the Q1 group. The results of the subgroup analyses showed that individuals aged 60 ~ 79 years, junior high school and above are more prone to suffer from cognitive impairment with a high RNT score. CONCLUSION: The study reveals a negative association between RNT and cognitive function in community-dwelling older adults. However, multi-center and a longer time span cohort studies on the relationship between RNT and cognitive function should be carried out to further explore the mechanisms involved.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".