The Management of Parkinson's Disease Before, during and after Pregnancy—an <scp>MDS</scp> Scientific Issues Committee Review
Bibliographic record
Abstract
BACKGROUND: Pregnancy after a Parkinson's diagnosis presents complex challenges. Due to the paucity of literature, there is no evidence-based guidelines and protocols for preconception care, management of pregnancy, childbirth and the postpartum period in women with early-onset Parkinson's disease (PD). Decision-making can be fraught with uncertainty for both patients and healthcare providers. OBJECTIVES: This review is aimed to provide pragmatic advice to help guide women with PD before, during and after pregnancy, and to address key gaps in the existing literature. METHODS: An interdisciplinary working group of movement disorder specialists, obstetricians, perinatal neuropsychiatrists, physiotherapist, pharmacist and individuals with lived experiences collaborated to assess published evidence. In areas lacking robust data, recommendations were derived from case studies, registries, clinical and personal expertise. RESULTS: Key recommendations include: Motor Symptom Management: Levodopa remains the safest treatment during the perinatal period. Monotherapy is preferred over polypharmacy. Non-Motor Symptom Management: Some non-motor symptoms are particularly common in this patient group and warrant individualized care. Preconception Considerations: Proactive planning about medical management should be done before conception. Genetic counseling and screening should be provided if desired. Peripartum and Postpartum Considerations: The decision regarding mode of delivery should be based upon women's birth plans and obstetric indications. Breastfeeding should be cautiously considered depending on the need for pharmacological treatment. CONCLUSIONS: This article provides a framework for managing PD before, during and after pregnancy. Collaborative efforts and ongoing registries like PregSpark* will be important to develop robust, evidence-based guidelines in this unique population.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".