Nonmedical Opioid Use After Short-term Therapeutic Exposure in Children: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Opioid-related harms continue to rise for children and youth. Analgesic prescribing decisions are challenging because the risk for future nonmedical opioid use or disorder is unclear. OBJECTIVE: To synthesize research examining the association between short-term therapeutic opioid exposure and future nonmedical opioid use or opioid use disorder and associated risk factors. DATA SOURCES: We searched 11 electronic databases. STUDY SELECTION: Two reviewers screened studies. Studies were included if: they were published in English or French, participants had short-term (≤14 days) or an unknown duration of therapeutic exposure to opioids before 18 years, and reported opioid use disorder or misuse. DATA EXTRACTION: Data were extracted, and methodologic quality was assessed by 2 reviewers. Data were summarized narratively. RESULTS: We included 21 observational studies (49 944 602 participants). One study demonstrated that short-term therapeutic exposure may be associated with opioid abuse; 4 showed an association between medical and nonmedical opioid use without specifying duration of exposure. Other studies reported on prevalence or incidence of nonmedical use after medical exposure to opioids. Risk factors were contradictory and remain unclear. LIMITATIONS: Most studies did not specify duration of exposure and were of low methodologic quality, and participants might not have been opioid naïve. CONCLUSIONS: Some studies suggest an association between lifetime therapeutic opioid use and nonmedical opioid use. Given the lack of clear evidence regarding short-term therapeutic exposure, health care providers should carefully evaluate pain management options and educate patients and caregivers about safe, judicious, and appropriate use of opioids and potential signs of misuse.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".