Development of the Comprehensive Addiction Risk Evaluation System: Initial Participant Response to an Online Personalized Feedback Program Integrating Genomic, Behavioral, and Environmental Risk Information
Bibliographic record
Abstract
Introduction: We have made tremendous advances in understanding the etiology of substance use disorders (SUDs). Despite these advances, screening for SUDs has remained largely unchanged. In this paper, we describe an effort to build a program that integrates advances across genomics, developmental psychology, and epidemiology to provide individuals with personalized information about their addiction risk profile. Methods: The program was developed based on foundational work from a NIDA-funded project that conducted multivariate analyses of externalizing phenotypes to advance gene identification for SUDs and then characterized how polygenic scores (PGS) and early life behavioral and environmental factors predicted SUDs in diverse longitudinal samples. Based on this work, we created PGS and a behavioral and environmental risk index to generate personalized risk profiles. We carefully considered ethical concerns when developing the program. Results: We created a user-friendly, self-directed online platform that provides personalized risk information, including overall risk for developing an SUD based on an individual's combination of genetic, behavioral, and environmental risk, and specific information about genetic risk, based on PGS, and behavioral/environmental risk. Data from the first 188 participants enrolled in an ongoing study to evaluate the platform indicate high satisfaction and low distress at receiving genetic information. Conclusion: Provision of personalized feedback about addiction risk factors, including genetic information along with behavioral and environmental feedback, may be a viable way to promote earlier screening and intervention with the goal of preventing substance use problems before they start.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".