Association between prescribed opioid use for acute pain in adolescents and the subsequent development of opioid misuse and substance use disorders: a systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: This review aims to examine prescribed short-term opioid use in adolescents to treat acute pain. The review will analyze the influence of opioid use on future non-medical opioid use (misuse) or substance use disorders (addiction) in adolescents and young adults. INTRODUCTION: Prescription opioids are medically indicated for acute pain. Descriptive studies of administrative datasets and surveys implicate adolescent opioid exposure as a risk factor for subsequent opioid misuse and addiction. This review will provide a synthesis of the literature on the association between prescribed opioid exposure to treat acute pain in adolescents and the subsequent development of opioid misuse or substance use disorders in adolescents and young adults. INCLUSION CRITERIA: This review will consider quantitative studies on opioid misuse or substance use disorders in Canadian and US adolescents and young adults (12 to 25 years of age). Studies must include exposure during adolescence (12 to 17 years of age) to legitimately prescribed short-term opioid use to treat acute pain. Studies on chronic pain or exposure to opioids for longer duration (more than 30 doses or more than 7 days) will be excluded. METHODS: This review will follow the JBI methodology for systematic reviews of etiology and risk. Published and unpublished studies will be sourced from multiple databases and resources. Two independent reviewers will screen, appraise, and extract data from studies that meet the inclusion criteria. Data synthesis will be conducted and a Summary of Findings will be presented. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42020179635.
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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 | 0.053 | 0.070 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.015 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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".