How do experts in psychiatric genetics view the clinical utility of polygenic risk scores for schizophrenia?
Bibliographic record
Abstract
Polygenic risk scores (PRS) are promising for identifying common variant-related inheritance for psychiatric conditions but their integration into clinical practice depends on their clinical utility and psychiatrists' understanding of PRS. Our online survey explored these issues with 276 professionals working in psychiatric genetics (RR: 19%). Overall, participants demonstrated knowledge of how to interpret PRS results. Their performance on knowledge-based questions was positively correlated with participants' self-reported familiarity with PRS (r = 0.21, p = 0.0006) although differences were not statistically significant (Wald Chi-square = 3.29, df = 1, p = 0.07). However, only 48.9% of all participants answered all knowledge questions correctly. Many participants (56.5%), especially researchers (42%), indicated having at least occasional conversations about the role of genetics in psychiatric conditions with patients and/or family members. Most participants (62.7%) indicated that PRS are not yet sufficiently robust for assessment of susceptibility to schizophrenia; most significant obstacles were low predictive power and lack of population diversity in available PRS (selected, respectively, by 53.6% and 29.3% of participants). Nevertheless, 89.8% of participants were optimistic about the use of PRS in the next 10 years, suggesting a belief that current shortcomings could be addressed. Our findings inform about the perceptions of psychiatric professionals regarding PRS and the application of PRS in psychiatry.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".