Reply to “Neocortical <scp>LBD</scp> in Women and Episodic Memory Decline in Men: Clarifying Sex Effects”
Bibliographic record
Abstract
We thank the authors for their thoughtful comments on our study demonstrating sex differences in the associations of Lewy Body disease (LBD) with Alzheimer's disease pathologies and cognitive decline.We appreciate the opportunity to clarify aspects of our findings.As described in the article, we dichotomized LBD pathology as "neocortical or limbic" versus "absent" in the main analyses, because both limbic and neocortical LBD appear to contribute to the clinical presentation of dementia with Lewy bodies.1,2 This approach helped minimize model complexity, particularly by limiting the number of interaction terms.Nonetheless, we recognize that examining LBD across specific stages may provide important mechanistic and clinical insights, particularly in clarifying which stages drive the observed sex-specific associations with tau pathology.To address this, we repeated the models examining the interactive effects of sex and LBD stage-categorized as neocortical, limbic, or absent-on neurofibrillary tangle burden (see the Table ).In both the NACC and ROS/MAP/MARS, female sex interacted with neocortical LBD (vs none) to associate with neurofibrillary tangles, whereas interactions with limbic LBD (vs none) were not significant.In sex-stratified analyses, in both datasets, neocortical LBD was associated with greater neurofibrillary tangle
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.032 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".