Relative frequency and risk factors for long-term opioid therapy following surgery and trauma among adults: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: When patients have been on opioid therapy for more than 90 days, more than half of them continue using opioids years later. Knowing that long-term opioid consumption could lead to harmful side effects including misuse, abuse, and addiction, it is important to understand the risks of transitioning to prolonged opioid therapy to reduce its occurrence. Perioperative and trauma contexts are ideal models commonly used to study such transition. Long-term use of opioids might be associated with transformation of acute pain to chronic, which might be an example of a risk factor. The objectives of this knowledge synthesis are to examine the relative frequency and the risk factors for transitioning to long-term opioid therapy among patients who have undergone a surgical procedure or experienced a trauma. METHODS: The proposed study methodology is based on Preferred ReportIng Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) statements on the conduct of systematic review and meta-analysis, the MOOSE Guidelines for Meta-Analyses and Systematic Reviews of Observational Studies, and the Cochrane Handbook for Systematic Review of Interventions. A systematic literature search will include multiple databases: Cochrane Central, EMBASE, MEDLINE, PsycINFO, CINHAL, PubMed, and the grey literature. We will identify studies related to opioid use beyond acute/subacute pain control after surgery or trauma. Two of the reviewers will screen all retrieved articles for eligibility and data extraction then critically appraise all identified studies. We will compile a narrative synthesis of all results and conduct a meta-analysis when feasible. As available data permits, we will perform a subgroup analysis of vulnerable populations. DISCUSSION: This systematic review will contribute to the prevention and harm reduction strategies associated with prescription opioids by identifying risk factors leading to the unwarranted long-term opioid therapy. The identification of common risk factors for long-term opioid therapy will help to orient further research on pain management as well as offer key therapeutic targets for the development of strategies to prevent prolonged opioid use. SYSTEMATIC REVIEW REGISTRATION: This protocol was registered in PROSPERO on March 2, 2018; registration number CRD42012018089907 .
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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 | 0.070 | 0.089 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.022 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.057 | 0.006 |
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".