Medications for Opioid Use Disorder in People Who Inject Substances: Reflection on the Potential Place of Morphine Sulfate as Substitution Treatment? Results of COSINUS Cohort Study
Bibliographic record
Abstract
BACKGROUND: Opioid Use Disorder (OUD) often provokes dramatic consequences in terms of increased morbi-mortality. Two medications have mainly been worldwide used for OUD (MOUD), buprenorphine and methadone. Recently, however, some reports have highlighted the use of Morphine Sulfate (MS) mainly obtained without a prescription but used as MOUD by opioid users and especially People Who Inject Substances (PWIS). We propose to characterize the prevalence and distribution of MOUD and MS use in PWIS. METHODS: This study examines the use of MOUD and MS amongst French PWIS recruited in harm reduction facilities and drug consumption rooms in the context of the COSINUS (Cohort to assess structural and individual factors in drug use) study. RESULTS: MOUD are prescribed, respectively, to one-third and one-fifth of PWIS, whereas a half of them declared MS consumption without prescription. MS users live with higher precariousness and are younger than non-users. MS is associated with salt cocaine and heroin use. It is often consumed with methadone and more rarely with buprenorphine and we hypothesized that this is probably linked to buprenorphine's pharmacological antagonism. DISCUSSION: Our results show the high prevalence of MS consumption and highlight the importance of considering the highly restricted possibility of prescribing MS as MOUD. Its association with methadone raises the question of their synergistic action on craving and mental disorders. The profiles of opioid users who could benefit from MS with or without methadone must be examined to improve their care but with the utmost caution, given the risk of overdose.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".