P.009 Women’s health in Parkinson’s Disease
Bibliographic record
Abstract
Background: Experimental and clinical evidence suggest that Parkinson’s disease (PD) manifests differently between females and males, yet women have been underrepresented in PD clinical research, leading to a limited understanding of the sex- and gender-specific aspects of the disease. Understanding the needs of women with PD (WwPD) is critical Methods: Patient-centered outcomes-based mixed methods study. Phase 1: Qualitative focus groups, patient-centered discussions, led by female interviewers. Phase 2: Nationwide survey via the Qualtrics platform, informed by focus group findings. We report the Phase 1 preliminary results Results: We conducted 5 focus groups with 22 cisgender women. Mean age 60.5 (range: 44 -81) and disease duration of 6.82 years. Two main themes emerged: (1) Mental Health: participants reported significant emotional distress, altered self-image, and impacts on family, social, and professional life. (2) Physical Health and Health care: While some were satisfied with care, those with young-onset PD experienced misdiagnosis, dismissal, and inadequate information. Sexual health, and the overlap between menopause and PD symptoms, were highlighted. Most participants emphasized the benefits of physical activity, nutrition, and social support. Conclusions: Findings highlight significant health challenges in women, underscoring the need for gender-specific care and tailored support to improve healthcare outcomes
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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".