Integrative exploration of bio-psycho-social determinants of DSM-5 severity levels of opioid use disorder: the BEBOP cohort study protocol
Bibliographic record
Abstract
INTRODUCTION: Opioid use disorder (OUD) is a chronic and severe psychiatric condition defined by a level of opioid use which significantly impairs interpersonal and social functioning. In the biopsychosocial model of addiction, research has shown that psychiatric, sociological and neurobiological factors individually affect OUD severity. However, how these factors interact in the determination of OUD severity remains poorly understood. METHOD AND ANALYSIS: The Epigenetic Bonds of Opioid Use Profiles are a multidisciplinary project whose primary objective is to characterise psychiatric and social factors of OUD in a large cohort of patients. The secondary objectives are, first, to correlate psychosocial severity with blood-derived epigenetic biomarkers to provide a deeper understanding of determinants of OUD and, second, to examine over a 2 year follow-up the correlation between the evolution of OUD and psychosocial severity with epigenetic biomarkers at inclusion. An additional objective is to analyse the impact of drug consumption rooms on access to care for most severely affected patients with OUD. In total, 300 opioid users will be recruited at supervised injection sites in Strasbourg and Paris and at addiction care centres in Strasbourg and Lyon to explore four psychiatric (substance use disorders beyond opioids, depression, anxiety, post-traumatic stress disorder) and five social (social support and status, traumatic experiences, housing, imprisonment, access to care) factors. Opioid users will be followed for 24 months and reassessed for psychosocial factors at 3, 6, 12, 18 and 24 months. Opioid consumption will be measured in all subjects using questionnaires, complemented by toxicological screenings (mass spectrometry). Finally, DNA methylation and gene expression will be characterised in capillary blood using next-generation sequencing. Mixed models will be used to model the primary and secondary outcomes. ETHICS AND DISSEMINATION: This ongoing study was approved by the French Ethics Committee 'Sud Méditerranée III' of University Hospital of Nîmes (approval 2023-2024, protocol IDRCB number 2022-A02477-36) and authorised by the French Data Protection Authority (authorisation decision DR-2023-277 in December 2023). Results will be presented in international and national conferences and published in peer-reviewed international journals. TRIAL REGISTRATION NUMBER: NCT06021548.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".