Plant-based natural products for symptomatic relief of Parkinson’s disease: prevalence, interest, awareness and determinants
Bibliographic record
Abstract
Abstract Natural health products (NHP) have emerged as a potential symptomatic therapeutic approach for persons with Parkinson’s disease (PwP). The objective of this study was to quantify the prevalence of ever use of NHP, interest in plant-based NHP, awareness of potential herb-drug interactions, and how often NHP use was discussed by PwP with their healthcare professionals. We addressed these objectives by embedding a cross-sectional 4-item survey within a large population-based cohort of PwP (PRIME-NL study). Sixty-five percent (n=367) of the 566 participants who were contacted completed the survey. Of those participants, 132 (36%) reported having used NHP to alleviate Parkinson’s disease (PD)-related symptoms, with coffee, cannabis and turmeric being the most popular. Overall, 12% (n=44) of PwP had used at least one NHP other than coffee or cannabis. Furthermore, 71% (n=259) participants expressed an interest in exploring the use of NHP, but only 39% (n=51) of NHP users were aware that these products could interact with PD medication. Finally, only 39% (n=51) of NHP users had discussed the use of NHP with their neurologist or PD nurse specialist. In a sensitivity analysis, we conservatively assumed that all non-responders to the survey had never used NHP and had no interest in exploring NHP. This rendered an estimated prevalence of NHP use of 23% and an estimated interest in exploring NHP of 46%. In conclusion, over one in three PwP has used NHP to alleviate symptoms of PD and the majority of PwP is interested in exploring the use of plant-based NHP. Most users had not discussed the intake of NHP with their PD healthcare professional and were unaware that these products could interact with PD medication. This study supports the need for evidence-based research on the properties of plant-derived therapeutics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".