Sex and gender differences in the molecular etiology of Parkinson’s disease: considerations for study design and data analysis
Bibliographic record
Abstract
Parkinson's disease (PD) is more prevalent in men than women, and presents with different clinical features in each sex. Despite widespread recognition of these differences, females are under-represented in clinical and experimental studies of PD, and much remains to be elucidated regarding the biological underpinnings of sex differences in PD. In this review, we summarize known contributors to sex differences in PD etiology across the life course, with a focus on neurological development and gene regulation. Sex differences that are established at conception and heightened during adolescence and midlife may partially embed future PD risk, due to the complex interactions between gonadal hormones, gene regulation, lifestyle factors, and aging. While the neuroprotective properties of estrogen are strongly implicated in reduced prevalence of PD in women, interactions with genotype and gender-biased lifestyle factors are incompletely understood. Consideration of sex and gender-related factors in study design, data analysis, and interpretation have the power to expedite our knowledge of the etiology of PD in men and in women, and to inform prevention and therapeutic strategies tailored to each sex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.092 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".