Data Sheet 1_Drug-induced Parkinson-like events: a real-world study from 2004 to the first quarter of 2024 based on FAERS.docx
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.014 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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; both teacher heads agree on what is shown here.
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".