Management of osteoporosis in Parkinson's disease: A scoping review
Bibliographic record
Abstract
BACKGROUND: Parkinson's Disease (PD) is a progressive neurodegenerative disorder that primarily affects the dopaminergic neurons in the basal ganglia. It leads to a range of motor symptoms such as tremors, bradykinesia, rigidity, and gait instability. A significant, but often overlooked, sequela of PD is osteoporosis, which contributes to a higher incidence of fractures. This risk is exacerbated by both PD itself as well as the medications used to manage PD. Despite the heightened risk of fragility fractures, osteoporosis in PD is frequently under-recognized and inadequately managed. OBJECTIVE: This review aimed to explore the current literature on osteoporosis management strategies specifically for individuals with PD. METHODS: MEDLINE, Scopus, and EMBASE were searched from database inception to August 10, 2025. RESULTS: Following a two-stage review process conducted by two independent reviewers, 18 relevant articles were identified. Of these, 17 were review articles and one was an interventional study. The literature most commonly identified lifestyle modifications, pharmacological treatments, and surgical interventions as management strategies for osteoporosis in PD. However, most of the recommendations were based on research conducted in the general population, raising concerns about their effectiveness for individuals with PD, who may have unique clinical needs. CONCLUSIONS: We note a significant gap in research focused on osteoporosis management in PD patients. Research efforts should prioritize developing tailored strategies to manage osteoporosis in individuals with PD.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".