High serum osmolarity and cognitive function among elderly community residents: The KOBE Study
Bibliographic record
Abstract
Abstract Background Few reports exist on the impact of chronic high serum osmolarity on cognitive decline. This study aimed to elucidate the relationship between chronic high serum osmolarity and the subsequent cognitive decline among elderly community residents. Methods This is a sub-analysis focusing on individuals aged 75 and older who underwent the Japanese version of the Montreal Cognitive Assessment (MoCA-J) between 2016 and 2020, as part of the Kobe Orthopedic and Biomedical Epidemiologic (KOBE) Study, in which biennial surveys had been conducted since 2010. The individuals were divided into two groups based on their MoCA-J scores: 22 or below and above 22. Using multivariate logistic regression models, we retrospectively examined the relationship between the MoCA-J scores and serum osmolarity obtained in the two surveys; 2012-2013 survey and 2016-2017 survey. Results A total of 214 individuals (120 females and 94 males) were included in this sub-analysis. Their average age was 76.2 ± 1.3 years, and the mean MoCA-J score was 24.5 ± 3.2. In the multivariate analysis using the data from the 2012-2013 survey, individuals with high serum osmolarity of 300 mOsm/L or higher had a significant association with the lower MoCA-J score (OR 2.70, 95% CI 1.30-5.60, p = 0.008). Similar results were obtained using the data from the 2016-2017 survey. Conclusions This study demonstrated that chronic high serum osmolarity is strongly associated with cognitive decline among elderly community residents. Therefore, proper control of serum osmolarity before reaching old age is considered useful for preventing cognitive decline. Key messages • Chronic high serum osmolarity is strongly associated with cognitive decline among elderly community residents. • Proper control of serum osmolarity before reaching old age is considered useful for preventing cognitive decline.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".