A Scoping Review of the Literature on Trauma Cue-Induced Drug Craving in Substance Users with Trauma Histories or PTSD
Bibliographic record
Abstract
Among trauma-exposed individuals, substances may be used as a means of obtaining symptom relief following exposure to trauma reminders. Repeated pairing of trauma cues with substance use may lead to the development of classically conditioned craving to trauma cues. Conditioned craving following cue exposure can be studied in-lab using the cue-reactivity paradigm. To map cue-reactivity research conducted with trauma-exposed substance users, we aimed to synthesize research which studied our population of interest, used a cue-reactivity paradigm, and measured craving as an outcome. Three databases were searched using relevant keywords. Twenty-eight studies met our criteria. Four key 19 themes are discussed in our review of these scoped studies—(1) craving as an outcome; (2) methodological subtypes across paradigms; (3) affect as an additional outcome or as a mediator of cue-induced craving; and (4) cue-reactivity paradigms as an intervention outcome assessment tool. Overall, there is strong evidence for cue-reactivity paradigms as a useful means of eliciting craving in response to trauma cues. Our scoping review suggests the need for a meta-analysis to determine the magnitude of the trauma cue-induced craving effect in substance users with trauma histories, and to determine significant moderators (e.g., PTSD symptom severity) and mediators of this effect (e.g., negative affect).
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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".