Which Sex- and Gender-Based Explanatory Variables Are Associated With Memory Function Poststroke? A Cross-Sectional Analysis of the National Health and Aging Trends Study
Bibliographic record
Abstract
OBJECTIVE: To identify sex- and gender-based variables associated with immediate and delayed recall in individuals with stroke. DESIGN: This was a secondary analysis of data from the National Health and Aging Trends Study (NHATS) using general linear models with a standard stepwise approach. SETTING: Community. INTERVENTIONS: Not applicable. PARTICIPANTS: Participants were eligible for the current analysis if they had a self-reported history of stroke at NHATS Round 1 (2011) and data available on our variables of interest. The final analyses included 366 participants for the immediate recall model and 365 participants for the delayed recall model. MAIN OUTCOME MEASURES: Independent variables of interest included sex- (8 variables, for example biological sex, depression and anxiety, and comorbidities) and gender-related factors (14 variables, for example education, income, and independence with banking). The dependent variables of interest were the 10-word immediate and delayed recall tests, respectively. RESULTS: Higher immediate recall scores were associated with younger age, female biological sex, independence with banking, higher income, giving financial gifts, not requiring assistance with activities of daily living, and higher education (P<.001-.04). Higher delayed recall scores were associated with younger age, higher body mass index, higher education, placing importance in socializing, and independence with banking (P<.001-.04). CONCLUSIONS: We conducted the largest analysis to date of sex- and gender-based factors associated with cognition in individuals with stroke. Stroke rehabilitation scientists and clinicians may consider both biological and sociodemographic factors associated with cognitive function, which may guide holistic poststroke assessments and interventions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".