Change in movement disorder specialist attitudes to genetic testing after implementation of PD GENEration
Bibliographic record
Abstract
Purpose: Despite advances in recent years, genetic testing for Parkinson disease (PD) is still underutilized in clinical practice. A 2019 questionnaire of movement disorder specialists found low rates of genetic testing in PD and barriers such as cost and insurance coverage. Since that time, PD GENEration, as well as several international programs, have broadly increased access to genetic testing and counseling for people with PD. A repeat survey sent out in 2024 examined how attitudes to genetic testing for PD in clinical practice have changed over the past 5 years. Methods: Between October 2024-January 2025, 621 movement disorders specialists from the Parkinson's Study Group (PSG) were invited by email to complete a questionnaire assessing knowledge, attitudes, and barriers to genetic testing in PD based on the 2019 survey. Results: In total, 119 PSG clinicians from the United States and Canada responded to the questionnaire. When compared with results from 2019, in 2024 both PD GENEration-affiliated and nonaffilated survey respondents reported fewer barriers to genetic testing both at the clinician and patient level. Conclusion: 2024 survey respondents report greater comfort with ordering and returning genetic test results compared with 2019, concurrent with the launch of PD GENEration, ROPAD, and GP2.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".