Environment, lifestyle, and Parkinson's disease: Implications for prevention in the next decade
Bibliographic record
Abstract
There is evidence from observational studies for a role of a number of environmental exposures and lifestyle habits in modulating the risk for Parkinson's disease. Environmental and lifestyle associations, if causal, represent opportunities for Parkinson's disease prevention or disease modification at individual and population levels. In the past decade, additional evidence has been published that improves causal inference and/or enhances our understanding of the complexity of these associations. A number of gene-environment interactions have been elucidated, and our understanding of the roles of physical activity, pesticide and other chemical exposures, dietary habits, emotional stress, head injury, and smoking has been refined. In the next decade, better techniques will help us to close the gaps in our knowledge, including taking into account Parkinson's disease heterogeneity and gene and risk factor interactions in observational studies. To do this, larger datasets, global consortia, genomewide environment interaction studies, prospective studies throughout the lifespan, and improvements in the methodology of clinical trials of physical activity will be key. Despite the caveats of observational studies, a number of low-risk and potentially high-yield recommendations for lifestyle modification could be made to minimize the individual and societal burdens of Parkinson's disease, including dietary modifications, increasing physical activity, and head injury avoidance. Furthermore, a reduction in pesticide use could have a major impact on global health related to and beyond Parkinson's disease. Given the increasing prevalence of this disorder, formulating and promoting these recommendations should be a high priority. © 2019 International Parkinson and Movement Disorder Society.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| 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".