Genetic testing for Parkinson disease: current practice, knowledge, and attitudes among US and Canadian movement disorders specialists
Bibliographic record
Abstract
PURPOSE: Genetic testing for Parkinson disease (PD) has not been widely used in clinical practice. In preparation for upcoming precision medicine-designed clinical trials for GBA and LRRK2, we evaluated movement disorders specialists' current practice, knowledge, attitudes, and barriers to genetic testing in PD. METHODS: An anonymous questionnaire was sent to movement disorders specialists at 146 Parkinson Study Group (PSG) sites in the United States (n = 131) and Canada (n = 15) to assess their knowledge and attitudes about genetic testing for PD. RESULTS: One hundred seventy-eight (47.6%) PSG clinicians completed the questionnaire. Forty-one percent of respondents had not referred any PD patients for genetic testing in the last year and >80% reported referring fewer than 11 patients over the same period. Most common reasons for not referring for genetic testing included lack of insurance coverage/cost to the patient and lack of perceived utility. On a scale of 0-100, the mean level of comfort in respondents' own ability to genetically counsel PD patients on GBA and LRRK2 was 52 (SD = 28). Sixty percent of clinicians correctly answered all questions about the inheritance and penetrance of GBA and LRRK2 variants. CONCLUSIONS: There is an urgent need to increase knowledge and reduce practical barriers to genetic counseling and testing in PD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".