Adverse Outcomes Associated with Prescription Opioids for Acute Low Back Pain: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
BACKGROUND: Acute low back pain (ALBP) is a common clinical complaint that can last anywhere from 24 hours to 12 weeks. In recent years, there has been an opioid epidemic which is linked to the increased availability of prescription opioids. Though guidelines recommend that in the treatment of ALBP, opioids should be used when other treatments fail, we have seen an increase in opioid prescriptions for ALBP. With this crisis, it is important to examine if there are any adverse outcomes associated with prescribing opioids for ALBP. OBJECTIVE: We aim to review the published literature to examine the adverse outcomes associated with opioid use for ALBP. STUDY DESIGN: We performed a systematic review with meta-analysis in accordance with our published protocol and PRISMA guidelines. SETTING: The review was conducted at McMaster University. METHODS: Various electronic databases for articles published from inception to September 30, 2017, inclusive. Both randomized clinical trials and observational studies on the impact of opioid use in ALBP in the adult population were included. Eight pairs of independent reviewers performed screening, data extraction, and assessment of methodological quality. The identified articles were assessed for risk of bias using sensitivity analysis. Trials with comparative outcomes were reported in a meta-analysis using a fixed effects model. RESULTS: A total of 13,889 studies were initially screened for the review and a total of 4 studies were included in the full review, of which 2 studies were meta-analyzed. Our results showed that prescribing opioids for ALBP was significantly associated with long-term continued opioid use (1.57, 95% CI, 1.06-2.33). There was no significant association found between unemployment duration and prescribing opioids for ALBP (3.54, 95% CI, -7.57 to 14.66). LIMITATIONS: Due to the limited number of studies that considered unemployment, only an unpooled analysis was conducted. Among the included studies there was both statistical and clinical heterogeneity due to differences in methodology, study design, risk of selection or performance bias. Most of the studies had an unclear or high risk of bias and poorly defined side effects. CONCLUSIONS: Due to the lack of literature examining long-term adverse outcomes associated with prescribing opioids for ALBP, no definitive conclusions can be made. However, with the literature available, there does seem to be risk associated with prescribing opioids for ALBP so there is a great need to conduct further investigations examining these adverse outcomes for ALBP patients. KEY WORDS: Acute low back pain, opioids, prescriptions, low back pain, long-term use, opioid use disorder.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.049 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".