Association Between Supportive Interventions and Healthcare Utilization and Outcomes in Patients on Long-Term Prescribed Opioid Therapy Presenting to Acute Healthcare Settings: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Long-term prescription of opioids by healthcare professionals has been linked to poor individual patient outcomes and high resource utilization. Supportive strategies in this population regarding acute healthcare settings may have substantial impact. Methods We performed a systematic review and meta-analysis of primary studies. The studies were included according to the following criteria: 1) age 18 and older; 2) long-term prescribed opioid therapy; 3) acute healthcare setting presentation from a complication of opioid therapy; 4) evaluating a supportive strategy; 5) comparing the effectiveness of different interventions; 6) addressing patient or healthcare related outcomes. We performed a qualitative analysis of supportive strategies identified. We pooled patient and system related outcome data for each supportive strategy.Results A total of 5664 studies were screened and 19 studies were included. A total of 9 broad categories of supportive strategies were identified. Meta-analysis was performed for the “supports for patients in pain” supportive strategy on two system-related outcomes using a ratio of means. The number of emergency department (ED) visits were significantly reduced for cohort studies (n = 6, 0.36, 95% CI [0.20-0.62], I2 = 87%) and randomized controlled trials (RCTs) (n = 3, 0.71, 95% CI [0.61-0.82], I2 = 0%). The number of opioid prescriptions at ED discharge was significantly reduced for RCTs (n = 3, 0.34, 95% CI [0.14-0.82], I2 = 78%).Interpretation For patients presenting to acute healthcare settings with complications related to long-term opioid therapy, the intervention with the most robust data is “supports for patients in pain”. Study registration CRD42018088962
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.045 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".