Sex similarities and differences in cognition: A longitudinal study of healthy control participants from the Parkinson Progression Markers Initiative
Bibliographic record
Abstract
BACKGROUND: Despite sex-based differences in age-related diseases and life expectancy, limited research has explicitly examined sex differences in aging. Longitudinal study designs are particularly underutilized. The current study retrieved longitudinal data from the healthy control group of the Parkinson's Progression Markers Initiative to examine baseline differences and cognitive changes in males and females over time. METHODS: Male (n = 125, mean age = 61.61) and female (n = 68, mean age = 59.44) participants completed neuropsychological measures annually for up to five years. Measures included the Montreal Cognitive Assessment (MoCA), Letter Number Sequencing (LNS), Semantic Fluency (SFT), Symbol Digit Modalities Test (SDMT), Benton Judgment of Line Orientation Test (BJLOT), Hopkins Verbal Learning Test-Revised Immediate and Delayed Recall (HVLT-R). Within-person changes in cognition and between-group differences longitudinal change trajectories as predicted by sex were examined in a hierarchical fashion. Effects of age and education were also examined. RESULTS: At baseline, females had higher scores on the SFT, SDMT, and the HVLT-R Immediate and Delayed Recall, while males had higher scores on the BJLOT. However, rates of change in cognition over time did not significantly differ by sex. Higher baseline age predicted lower scores for all neuropsychological outcome measures, and higher education predicted higher scores for all neuropsychological outcome measures except for the MoCA. CONCLUSIONS: Although there were sex differences in certain domains of cognitive function, rates of cognitive change over time did not significantly differ by sex. Intraindividual variability in cognitive trajectories of aging was observed. Future research should examine factors that predict individual trajectories of aging in healthy individuals.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".