The impact of receiving polygenic risk scores for alcohol use disorder on psychological distress, risk perception, and intentions to reduce drinking
Bibliographic record
Abstract
For the return of polygenic risk scores to become an acceptable clinical practice in psychiatry, receipt of polygenic risk scores must be associated with minimal harm and changes in behavior that decrease one's risk for developing a psychiatric outcome. Data from a randomized controlled trial was used to assess the impact of different levels of hypothetical polygenic risk scores for alcohol use disorder on psychological distress, risk perception, and intentions to change drinking behaviors. The analytic sample consisted of 325 participants recruited from an urban, public university. Results demonstrated that there were significant increases in psychological distress as the level of genetic risk for alcohol use disorder increased. In addition, the perceived chance of developing alcohol use disorder significantly increased as the level of genetic risk increased. Promisingly, a greater proportion of participants indicated that they would intend to engage in follow-up behaviors, such as seeking additional information, talking to a healthcare provider about risk, and reducing drinking behaviors, as the level of genetic risk increased. Returning polygenic risk scores for alcohol use disorder in a clinical setting has the potential to promote risk-reducing behavior change, especially with increasing levels of genetic risk. The study was registered on ClinicalTrials.gov (Identifier: NCT05143073).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".