A Holistic Wellness Prescription for Parkinson's Disease: Evidence‐Based Perspectives and Unmet Needs
Bibliographic record
Abstract
BACKGROUND: In modern medicine the concept of wellness is often accompanied by various misconceptions arising from several factors, including a lack of clear definitions, the commercialization of wellness, and prevailing biases and stereotypes. METHOD: Although wellness has been successfully integrated into the management of conditions like cardiovascular disease, diabetes, and cancer, it has yet to be widely applied in the field of neurology. The World Federation of Neurology, American Academy of Neurology, European Academy of Neurology, and the World Health Organization (WHO) have adopted a formal definition of brain health that emphasizes the proactive role of lifestyle choices in modifying outcomes for neurological diseases, which closely aligns with the wellness approach. This shift has been further reinforced by WHO's adoption of the Intersectoral Global Action Plan on epilepsy and other neurological disorders (2022-2031), which seeks to improve access to treatment and care while promoting brain health across the lifespan. The global push for brain health highlights the need for a structured approach to wellness in neurological conditions such as Parkinson's disease (PD). RESULTS/CONCLUSION: This critical review, conducted by a multidisciplinary task force commissioned by the International Parkinson and Movement Disorder Society, aims to provide the current evidence base on wellness in PD, identify existing gaps in knowledge, and propose a framework to integrate wellness into the holistic care of 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".