Health harms of non‐medical prescription opioid use: A systematic review
Bibliographic record
Abstract
ISSUES: Non-medical prescription opioid use (NMPOU) contributes substantially to the global burden of morbidity. However, no systematic assessment of the scientific literature on the associations between NMPOU and health outcomes has yet been undertaken. APPROACH: We undertook a systematic review evaluating health outcomes related to NMPOU based on ICD-10 clinical domains. We searched 13 electronic databases for original research articles until 1 July 2021. We employed an adaptation of the Oxford Centre for Evidence-Based Medicine 'Levels of Evidence' scale to assess study quality. KEY FINDINGS: Overall, 182 studies were included. The evidence base was largest on the association between NMPOU and mental and behavioural disorders; 71% (129) studies reported on these outcomes. Less evidence exists on the association of NMPOU with infectious disease outcomes (26; 14%), and on external causes of morbidity and mortality, with 13 (7%) studies assessing its association with intentional self-harm and 1 study assessing its association with assault (<1%). IMPLICATIONS: A large body of evidence has identified associations between NMPOU and opioid use disorder as well as on fatal and non-fatal overdose. We found equivocal evidence on the association between NMPOU and the acquisition of HIV, hepatitis C and other infectious diseases. We identified weak evidence regarding the potential association between NMPOU and intentional self-harm, suicidal ideation and assault. DISCUSSION AND CONCLUSIONS: Findings may inform the prevention of harms associated with NMPOU, although higher-quality research is needed to characterise the association between NMPOU and the full spectrum of physical and mental health 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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".