Association between harm reduction strategies and healthcare utilization in patients on long-term prescribed opioid therapy presenting to acute healthcare settings: a protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Opioids are routinely used to treat a variety of chronic conditions associated with pain. However, they are a class of medications with a significant potential for adverse health effects, with and without misuse. Opioid misuse, as defined as inappropriate use of appropriately prescribed opioids, is becoming more well-recognized publicly but does not have clear treatment options. Opioid misuse has been linked to variety of poor outcomes and its consequences have a significant impact on healthcare resource utilization. The evidence on harm reduction strategies to mitigate adverse events prompting presentation to acute care settings for patients presenting with long-term opioid use is sparse. METHODS AND ANALYSIS: We will perform a systematic review and meta-analysis to catalog effective harm reduction strategies and identify the most effective ones to reduce avoidable healthcare utilization in patients on long-term opioid therapy who present to acute health care settings with complications attributed to opioid misuse. A search strategy will be developed and executed by an information specialist; electronic databases (MEDLINE, EMBASE, CINAHL, Cochrane Library) and additional sources will be searched. Search themes will include opioids, chronic drug use, and acute healthcare settings. Citation screening, selection, quality assessment, and data abstraction will be performed in duplicate. A comprehensive inventory of harm reduction strategies will be developed. Data will be collected on patient-related outcomes associated with each identified harm reduction strategy. When sufficiently homogeneous data on interventions, population, and outcomes is available, it will be pooled for aggregate analysis. Evaluation of the methodological quality of individual studies and of the quality of the body of evidence will be performed. Our primary objective will be to identify harm reduction strategies that have been shown to result in clinically relevant and statistically significant improvements in patient outcomes and/or decreased healthcare utilization. DISCUSSION: This study will better characterize harm reduction strategies for patients on long-term prescribed opioids presenting to acute healthcare settings. It will also add new knowledge and generate greater understanding of key knowledge gaps of the long-term prescribed opioid use and its impact on healthcare utilization. SYSTEMATIC REVIEW REGISTRATION: CRD42018088962 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".