Drug-induced Parkinson-like events: a real-world study from 2004 to the first quarter of 2024 based on FAERS
Bibliographic record
Abstract
Background: Timely identification of drug-induced Parkinson-like events is essential to improve clinical management and enhance patients' quality of life. However, there is a significant lack of studies addressing these events in real-world settings. Methods: To bridge this gap, we analyzed adverse event (AE) reports related to Parkinson-like events from the FDA Adverse Event Reporting System (FAERS) database from the first quarter of 2004 to the first quarter of 2024. Our objective was to summarize a list of potential drugs at high risk for Parkinson-like events and their corresponding proportions of AE reports. Results: As a result, a total of 54,639 AE reports linked to Parkinson-like events involving 1,224 drugs were identified. Among these, carbidopa/levodopa exhibited the highest number of reports, followed by baclofen and pimavanserin. The most frequently reported drug class was antiparkinsonian drugs and psycholeptics, followed by psychoanaleptics. Using two disproportionate analysis methods, the reporting odds ratio and proportional reporting ratio, we found that 136 drugs exhibited positive results in both methods, while 1,063 drugs did not show any positive signals. Conclusion: This study provides a comprehensive pharmacovigilance analysis of drugs associated with Parkinson-like events, aiming to promote rational drug use and inform clinical practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".