Sex and Gender considerations in Lewy Body Dementia: a perspectives paper
Bibliographic record
Abstract
Abstract Background Lewy Body Dementia (LBD) is a complex neurodegenerative disorder marked by fluctuating cognition, parkinsonism, and visual hallucinations. Despite being the second most common form of dementia, it remains misunderstood and underdiagnosed. Existing diagnostic and therapeutic approaches rarely account for sex and gender differences, which may contribute to delayed diagnosis and suboptimal care, particularly for women. Understanding how sex and gender interact with LBD mechanisms and patient experiences is essential for developing equitable, person-centred interventions. Methods This study employed a structured evidence synthesis, drawing from peer-reviewed literature on LBD with a focus on sex- and gender-related variables. A comprehensive search across PubMed, Scopus, and Web of Science identified original research, systematic reviews, and meta-analyses examining biological (e.g., hormonal, genetic, and neurochemical) and gender-related (e.g., caregiving roles, health-seeking behaviours, stigma) factors in LBD. Data were analysed through narrative synthesis, triangulating findings across clinical presentation, lived experiences, and pathophysiological mechanisms. Results Males with LBD are more likely to exhibit parkinsonian symptoms and visual hallucinations, whereas females face greater mood disturbances and caregiver-related stress; moreover, diagnostic tools tend to underrepresent female symptomatology. Our findings reveal that, contrary to previous assumptions of uniformity, there are substantial sex and gender-based variations in LBD’s pathophysiology and patient experience, impacting both disease recognition and management. This adds to prior knowledge by demonstrating that both biological and sociocultural factors must be considered to improve outcomes. Conclusion Sex and gender are fundamental, yet underexplored, dimensions of LBD. This paper proposes a conceptual framework integrating biological sex factors with sociocultural gender influences. By examining their interplay, our perspective highlights the importance of integrating sex and gender into LBD research and care. This approach not only improves diagnostic accuracy and treatment effectiveness, but also advances equity and strengthens support systems for both patients and caregivers.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".