Adverse effects of medications used to treat motor symptoms of Parkinson's disease: A narrative review
Bibliographic record
Abstract
Abstract BACKGROUND: In the 1960s, levodopa was first administered to treat the symptoms of Parkinson’s disease (PD), and it has since become the “gold standard” in its treatment. Since then, many classes of drugs have been made available to treat PD; however, these drugs are associated with considerable adverse effects. OBJECTIVE: The objective of this review is to highlight the most important and clinically relevant side effects of the medications used to treat the motor symptoms of PD. MATERIAL AND METHODS: We used PubMed and Google scholar to search for articles from January 1975 to January 2021. RESULTS: The medications used to treat PD vary in their mechanisms of action. The major classes of drugs that are used include levodopa and dopamine agonists. Nausea, vomiting, sleepiness, and neuropsychiatric and cardiovascular problems are some of the most common adverse effects observed. In addition, class-specific adverse effects of various drugs are observed and are important. CONCLUSIONS: The drugs used to treat PD are associated with considerable adverse effects, which may be mild, severe, or even life threatening. Most adverse effects are reversible and disappear with drug withdrawal. However, discontinuation of the drugs may not always be possible. Education of the patient and caregiver and awareness among clinicians is essential for early recognition and to prevent impairment of the quality of life. In addition, the development of new drugs with a favorable side effect profile should be prioritized.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".