Analyzing the correlation of vitamin D and cognitive function in community elders over 60 years old
Bibliographic record
Abstract
Abstract Background Dementia is a syndrome which describes a decline about cognition severe enough to affect daily living ability. The treatment for dementia is limited; Looking for factors related to cognitive dysfunction, controlling or reducing these factors will help delay the disease. Method We collected 190 community elders over 60 years old, used Montreal cognitive assessment (MOCA) to evaluated cognition, and divided into normal cognitive function group and abnormal cognitive function group. And the same time blood samples were collected to measure vitamin D. Compared the vitamin D between the two groups, P<0.05 was significant difference .Pearson's index was used to further compare the vitamin D and a single cognitive domain, P<0.05 was significant statistically. Result The incidence of cognition impairment was 77.9%, Comparing to normal cognitive function group and cognitive impairment group, the Vitamin D was significant differences(P<0.05).Further Pearson correlation analysis found that multiple cognitive domains such as executive, calculation, abstraction, visual space, naming, and attention were Positively correlated with vitamin D(P<0.05). Conclusion Cognitive function is closely related to vitamin D, especially executive, orientation, calculation, abstraction, visual space, naming, and attention, adequate vitamin D may delay cognitive decline.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".