Strategies aimed at preventing chronic opioid use in trauma and acute care surgery: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Globally every year, millions of patients sustain traumatic injuries and require acute care surgeries. A high incidence of chronic opioid use (up to 58%) has been documented in these populations with significant negative individual and societal impacts. Despite the importance of this public health issue, optimal strategies to limit the chronic use of opioids after trauma and acute care surgery are not clear. We aim to identify existing strategies to prevent chronic opioid use in these populations. METHODS AND ANALYSIS: We will perform a scoping review of peer-reviewed and non-peer-reviewed literature to identify studies, reviews, recommendations and guidelines on strategies aimed at preventing chronic opioid use in patients after trauma and acute care surgery. We will search MEDLINE, EMBASE, PsycINFO, CINHAL, Cochrane Central Register of Controlled Trials, Web of Science, ProQuest and websites of trauma and acute care surgery, pain, government and professional organisations. Databases will be searched for papers published from 1 January 2005 to a maximum of 6 months before submission of the final manuscript. Two reviewers will independently evaluate studies for eligibility and extract data from included studies using a standardised data abstraction form. Preventive strategies will be classified according to their types and targeted trauma populations and acute care surgery procedures. ETHICS AND DISSEMINATION: Research ethics approval is not required as this study is based on the secondary use of published data. This work will inform research and clinical stakeholders on the required next steps towards the uptake of effective strategies aimed at preventing chronic opioid use in trauma and acute care surgery patients.
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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 | 0.082 | 0.063 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.012 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.010 |
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".