<i>Mucuna pruriens</i> in untreated Parkinson's disease in sub-Saharan Africa: A 12-month, multicenter, randomized, controlled trial
Bibliographic record
Abstract
Background Parkinson's disease (PD) causes disability and premature mortality if untreated. Limited access to levodopa in low- and middle-income countries leaves many patients undertreated. Mucuna pruriens (MP) is a leguminous plant that contains high concentrations of levodopa. Objective To demonstrate the non-inferiority of long-term intake of MP powder in terms of safety and efficacy compared to standard levodopa plus dopa-decarboxylase inhibitor (LD + DDCI). Methods In this 12-month, multicenter, randomized, open-label phase 2 trial, thirty-two untreated PD patients received levodopa monotherapy with MP powder -derived from roasted seeds without pharmacological processing- or standard LD + DDCI. Dosing was adjusted for body weight and disease stage, with MP doses further calibrated to account for the absence of a DDCI. We measured quality of life using the 39-item PD Questionnaire, motor and non-motor disability using the Movement Disorders Society updated version of the Unified PD Rating Scale (MDS-UPDRS) (parts I to IV) and the Non-Motor Symptoms Questionnaire. Safety measures included recording any adverse event and laboratory test. Results MP powder improved quality of life, motor and non-motor symptoms over 12 months, demonstrating similar outcome to LD + DDCI on all endpoints. Adverse events were more frequent with MP (56% vs. 37.5%, p = 0.48), though the difference was not statistically significant. Most were mild, with only 12.5% leading to discontinuation. Conclusions MP could be a cost-effective alternative for PD individuals with limited access to commercial levodopa formulations. To confirm long-term safety and efficacy, larger international multicenter, double-blind trials with extended follow-up (e.g. 24–36 months) and ethnically diverse cohorts are needed. Registered at PACTR201611001882367
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".