Association Between the Number of Deliveries and Cognitive Impairment Considering the Presence of Subclinical Cerebrovascular Diseases: The Ohasama Study
Bibliographic record
Abstract
Although the association between the number of deliveries and cognitive impairment has been previously examined, the influence of subclinical cerebrovascular diseases (SCDs), such as silent cerebrovascular lesions and carotid atherosclerosis, on this association remains unclear. This cross-sectional study aimed to examine whether SCDs mediated the association between the number of deliveries and cognitive impairment. Among 627 Japanese women with a mean age of 73 years, the number of deliveries was collected in the 1998 survey and classified into four groups (0-1, 2, 3, ≥ 4), with two deliveries as the reference. At the annual comprehensive medical examinations, cognitive function was assessed using the Mini-Mental State Examination (MMSE), and SCDs were evaluated using brain magnetic resonance imaging and ultrasonography. Each participant's latest data on these variables and covariates between 1992 and 2018 were used. MMSE scores were divided into three ordinal categories: ≥ 28 (normal), 24-27 (mild cognitive impairment; MCI), and ≤ 23 (severe cognitive impairment). Ordinal logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for cognitive impairment. The ORs for cognitive impairment associated with the number of deliveries were 2.13 (95% CI, 1.21-3.76) in the lowest (0-1) group and 1.45 (0.95-2.23) in the highest (≥ 4) group. These association estimates remained similar after adjusting for SCDs but were weaker in the more recent birth year group. We demonstrated a U-shaped association between the number of deliveries and cognitive impairment, independent of SCDs, and the cohort effect confounded the association.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".