Head circumference, leg length and its association with dementia among older adult population in Singapore
Bibliographic record
Abstract
BACKGROUND: Head circumference and leg length serve as reliable proxy indicators of early-life environment. Research studies have shown that these anthropometric measurements are associated with cognitive impairment and dementia among older adults. The aim of the present study was to assess the associations between dementia with head circumference and leg length among the older adult population in Singapore. This study also aimed to examine the sociodemographic correlates of these anthropometric measurements. METHODS: Data were collected from 2565 older adults aged 60 years and above, in a population study on the Well-being of the Singapore Elderly. Head circumference and leg length measurements were obtained, and sociodemographic information was recorded. Dementia diagnosis was made using the 10/66 dementia algorithm. Anthropometric measurements were first stratified into quarters, and then logistic regression analysis was used to examine factors associated with head circumference and leg length, as well as to examine the association between dementia with these measurements. RESULTS: Sociodemographic correlates of head circumference and leg length include age, gender, ethnicity and education level. Smaller head circumference was independently associated with higher odds of 10/66 dementia (OR = 2.173-2.709). When the regression analysis was stratified by gender, the association was found only in the male sample. Leg length was not significantly associated with dementia after controlling for sociodemographic variables. CONCLUSION: Smaller head circumference is independently associated with dementia among older adults in Singapore. Findings from this study suggest that risk factors for dementia begin their influence in early life. Copyright © 2017 John Wiley & Sons, Ltd.
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 | 0.001 |
| 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".