Efficacy of prescribed opioids for acute pain after being discharged from the emergency department: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Opioids are often prescribed for acute pain to patients discharged from the emergency department (ED), but there is a paucity of data on their short-term use. The purpose of this study was to synthesize the evidence regarding the efficacy of prescribed opioids compared to nonopioid analgesics for acute pain relief in ED-discharged patients. METHODS: MEDLINE, EMBASE, CINAHL, PsycINFO, CENTRAL, and gray literature databases were searched from inception to January 2023. Two independent reviewers selected randomized controlled trials investigating the efficacy of prescribed opioids for ED-discharged patients, extracted data, and assessed risk of bias. Authors were contacted for missing data and to identify additional studies. The primary outcome was the difference in pain intensity scores or pain relief. All meta-analyses used a random-effect model and a sensitivity analysis compared patients treated with codeine versus those treated with other opioids. RESULTS: From 5419 initially screened citations, 46 full texts were evaluated and six studies enrolling 1161 patients were included. Risk of bias was low for five studies. There was no statistically significant difference in pain intensity scores or pain relief between opioids versus nonopioid analgesics (standardized mean difference [SMD] 0.12; 95% confidence interval [CI] -0.10 to 0.34). Contrary to children, adult patients treated with opioid had better pain relief (SMD 0.28, 95% CI 0.13-0.42) compared to nonopioids. In another sensitivity analysis excluding studies using codeine, opioids were more effective than nonopioids (SMD 0.30, 95% CI 0.15-0.45). However, there were more adverse events associated with opioids (odds ratio 2.64, 95% CI 2.04-3.42). CONCLUSIONS: For ED-discharged patients with acute musculoskeletal pain, opioids do not seem to be more effective than nonopioid analgesics. However, this absence of efficacy seems to be driven by codeine, as opioids other than codeine are more effective than nonopioids (mostly NSAIDs). Further prospective studies on the efficacy of short-term opioid use after ED discharge (excluding codeine), measuring patient-centered outcomes, adverse events, and potential misuse, are needed.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".