Exploration des mécanismes potentiels sous-jacents aux troubles liés à l’utilisation d’une substance chez les personnes atteintes d’un trouble de la personnalité
Bibliographic record
Abstract
Background Cluster-B personality disorders (DSM-V), particularly borderline and antisocial personality disorders, are associated with high rates of substance use disorder. However, the mechanisms underlying this relationship have yet to be fully understood. Craving has recently been identified as an important component of substance use disorder. The purpose of this article is therefore to review the current literature and explore whether craving could be implicated as an underlying mechanism of comorbid substance use disorder in antisocial and borderline personality disorders.Method Critical review of the literature.Results Emerging evidence indicates that there is in fact an association between craving and personality disorders. Patients afflicted with the latter, incidentally, respond differently to anti-craving medication when compared to normal subjects. While a limited number of studies have directly assessed craving in patients with personality disorders, a growing number have looked at the association between craving and specific personality traits. The correlation between impulsivity, negative affect and craving seems like a plausible explanation for the high prevalence and severity of substance use disorder in subjects with antisocial or borderline personality disorders.Conclusion These findings suggest that specific personality traits are related to craving and could represent promising targets for the prevention, assessment and management of comorbid substance use disorders.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".