Relationship between cumulative ultraviolet exposure and cognitive function in a rural elderly Chinese population
Bibliographic record
Abstract
OBJECTIVES: Some researchers have focused on the relationship between vitamin D and cognition, but the conclusions are inconsistent. We estimated cumulative UV exposure could be used to represent the individual's long-term vitamin D status and investigated its association with global cognitive function in elderly Chinese. METHODS: A total of 641 participants aged 60 years and over were recruited in a rural area of Shenyang, China. All were interviewed to obtain data regarding sociodemographic characteristics and time spent outdoors. Cognitive function was evaluated using the Montreal Cognitive Assessment-Beijing version (MoCA-BJ). Images of skin from UV-exposed (dorsal hand) and UV-protected (inner forearm) sites from each individual were graded by the Beagley-Gibson system. Differences in skin-grade between the 2 sites were used to indicate cumulative UV exposure level. Subjects were grouped in tertiles based on skin-grade differences (<1.75, 1.75-2.74, and ≥ 2.75), representing low, medium, and high UV exposure levels, respectively. The MoCA-BJ score was classified in tertiles as low (<19), middle (19-22), and high (≥23) levels of cognition. Associations between cognitive function and UV exposure were analyzed using ordinal regression. RESULTS: Skin-grade differences were associated with self-reported time spent outdoors. After adjustment for age, gender, education, BMI, whether living alone, income, diet, hypertension, and diabetes, a high UV exposure level was associated with better cognitive function (odds ratio = 0.643, 95% confidence interval = 0.427-0.969). CONCLUSIONS: Greater cumulative UV exposure appears to be associated with better cognitive function in elderly adults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".