Modeling Attention Performance Across Female Reproductive Aging Using Logistic Regression
Bibliographic record
Abstract
Reproductive aging in women is associated with cognitive decline, particularly in attention domains. This study investigates the use of machine learning, specifically logistic regression, to predict attentional performance during different reproductive aging stages. A total of 100 healthy women, 50 premenopausal (mean age$=29.5)$and 50 postmenopausal (mean age$=54.8)$, underwent neuropsychological evaluation using the Montreal Cognitive Assessment (MoCA) and the Integrated Visual and Auditory (IVA-2) test. Among the applied supervised models, logistic regression achieved 95.0% accuracy in 10-fold crossvalidation and 100% on the test set. The analysis revealed a strong inverse correlation between reproductive aging and auditory attention subscores. These results suggest that logistic regression offers a reliable, interpretable, and clinically applicable model for early detection of attention decline, supporting its integration into biomedical screening tools.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".