Genetic Testing in Parkinson's Disease
Bibliographic record
Abstract
Genetic testing for persons with Parkinson's disease is becoming increasingly common. Significant gains have been made regarding genetic testing methods, and testing is becoming more readily available in clinical, research, and direct-to-consumer settings. Although the potential utility of clinical testing is expanding, there are currently no proven gene-targeted therapies, but clinical trials are underway. Furthermore, genetic testing practices vary widely, as do knowledge and attitudes of relevant stakeholders. The specter of testing mandates financial, ethical, and physician engagement, and there is a need for guidelines to help navigate the myriad of challenges. However, to develop guidelines, gaps and controversies need to be clearly identified and analyzed. To this end, we first reviewed recent literature and subsequently identified gaps and controversies, some of which were partially addressed in the literature, but many of which are not well delineated or researched. Key gaps and controversies include: (1) Is genetic testing appropriate in symptomatic and asymptomatic individuals without medical actionability? (2) How, if at all, should testing vary based on ethnicity? (3) What are the long-term outcomes of consumer- and research-based genetic testing in presymptomatic PD? (4) What resources are needed for clinical genetic testing, and how is this impacted by models of care and cost-benefit considerations? Addressing these issues will help facilitate the development of consensus and guidelines regarding the approach and access to genetic testing and counseling. This is also needed to guide a multidisciplinary approach that accounts for cultural, geographic, and socioeconomic factors in developing testing guidelines. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".