Adverse effects of opioids prescribed in opioid agonist treatment: a systematic review protocol
Bibliographic record
Abstract
Background: Opioid agonist treatment (OAT) comprises the use of opioid agonists to replace illicit street opioids and is the treatment of choice for individuals with opioid use disorder (OUD). We aim to systematically review the risk for any type of adverse effects of OAT in patients with opioid use disorder.Methods: We aim to inform evidence-based care decisions with a systematic review of published randomised controlled trials (RCTs) evaluating opioid agonists for the treatment of OUD in comparison to any control and reporting adverse effects. The search strategy was developed and peer-reviewed by medical information specialists. The databases Embase, Medline, PsycInfo, CENTRAL, and the Web of Science Core Collection will be searched without date limits. Title and abstract screening will be done in duplicate by three and full text screening by two independent reviewers. Data extraction will be checked by a second reviewer. Disagreements will be resolved by a third reviewer.We aim to combine study results in random-effects meta-analyses and calculate relative and absolute risks.Risk of bias will be assessed with version 2 of the Cochrane risk-of-bias tool for randomized trials (RoB 2).An assessment of quality of evidence will be conducted using the Grading of Recommendation, Assessment, Development and Evaluation (GRADE) framework.Discussion: The results of this systematic review will provide evidence to allow optimal clinical decision making for patients with OAT, in particular those with previous adverse events.
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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 | 0.069 | 0.064 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.017 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.075 | 0.011 |
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".