Psychiatric polygenic risk scores: Child and adolescent psychiatrists’ knowledge, attitudes, and experiences
Bibliographic record
Abstract
Abstract Objective Psychiatric polygenic risk scores (PRS) have the potential to transform aspects of psychiatric care and prevention, but there are concerns about their implementation. We sought to assess child and adolescent psychiatrists’ (CAP) experiences, perspectives, and potential uses of psychiatric PRS. Methods A survey of 960 US-based practicing CAP. Results Most respondents (54%) believed psychiatric PRS are currently at least slightly useful and 87% believed they will be so in five years. Yet, 77% rated their knowledge of PRS as poor or very poor. Ten percent have had a patient/family bring PRS to them, and 25% would request PRS if a patient/caregiver asked. Respondents endorsed different actions in response to a hypothetical child with a top 5 th percentile psychiatric PRS but no diagnosis: 48% would increase prospective monitoring of symptoms, 42% would evaluate for current symptoms, and 4% would prescribe medications. Most respondents were concerned that high PRS results could lead to overtreatment and negatively impact patients’ emotional well-being. Conclusion Findings indicate emerging use of psychiatric PRS within child and adolescent psychiatry in the US. Thus, it is critical to examine the ethical and clinical challenges that PRS may generate and begin efforts to promote their informed and responsible use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".