Moderators for the Relationship between Post-Traumatic Stress Disorder and Opioid Use Disorder
Bibliographic record
Abstract
Objective: Post-traumatic stress disorder (PTSD) is a common risk factor for opioid use disorder (OUD). However, not all individuals with PTSD develop OUD when exposed to opioids. As the underlying moderators remain unexplored, this analysis aimed to determine if non-traumatic adverse experiences and stressors prior to the age of 18 moderate the relationship between PTSD and OUD. Methods: In a matched dataset (n = 830) of individuals with or without PTSD who reported lifetime use of opioids, the following non-traumatic adverse experiences and stressors were assessed: emotional abuse, emotional neglect and physical neglect, parents’ adverse experiences, and number of days jailed before the age of 18. Using the PROCESS macro in SAS for each factor, the conditional effects were estimated through simple slopes. Moderation was inferred through significant interaction effects. Results: The matched data were similar on age, gender, ethnicity, education, being born in the US, living with, or losing biological parents before age 18, and family history of depression, anxiety, and substance use disorder. Significantly more individuals in the preexisting PTSD group had preexisting psychiatric disorders, and preexisting substance use and schizotypal personality disorder. Childhood emotional abuse and neglect and physical neglect (effect: 0.03; 95%CI: 0.001−0.056; p = .039), and more than one event of adversity experienced by parents (effect: 0.34; 95%CI: 0.07−0.61; p = .013) significantly interacted with PTSD to lead to OUD. Conclusion: The conditional effect of PTSD on the development of OUD after exposure to opioids was dependent on the frequency and severity of childhood non-traumatic adverse experiences. To identify individuals with PTSD who are at a high risk of developing OUD, programs may focus on non-traumatic adverse childhood experiences that are not commonly explored. Future steps may include focusing on educational schemes to mitigate this higher risk of developing OUD in at-risk individuals, for example, by discussing the risks when prescribing opioids.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".