Interventions to reduce opioid use for patients with chronic non-cancer pain in primary care settings: A systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: This systematic review and meta-analysis aimed to assess interventions to reduce opioid use for patients with chronic non-cancer pain (CNCP) versus usual care or active controls in primary care settings. METHODS: In this registered study (PROSPERO: CRD42022338458), we searched MEDLINE, Embase PsycInfo, CINAHL, and Cochrane Library from inception to December 28th 2021, and updated on Dec 14th 2023 for randomized controlled trials (RCTs) and cohort studies with no restrictions. Methodological quality was assessed using the Cochrane Risk of Bias tool for RCTs and Newcastle Ottawa Scale for cohort studies. Primary outcomes included mean reduction in morphine equivalent daily dose (reported as mean differences [MDs] mg/day; 95% confidence intervals [95%CIs]) and/or opioid cessation proportion. Secondary outcomes were mean changes in pain severity (reported as standardized mean difference [SMDs]; 95%CIs) and (serious) adverse events. Meta-analyses were performed using random-effects models. RESULTS: We identified 3,826 records, of which five RCTs (953 participants) and five cohort studies (901 participants) were included. Overall, opioid dosage was significantly reduced in intervention groups compared to controls (MD: -28.63 mg/day, 95%CI: -39.77 to -17.49; I2 = 31.25%; eight studies). Subgroup analyses revealed significant opioid dose reductions with mindfulness (MD: -29.36 mg/day 95%CI: -40.55 to -18.17; I2 = 0.00%; two trials) and CBT-based multimodalities (MD: -41.68 mg/day; 95%CI: -58.47 to -24.89; I2 = 0.00%; two cohort studies), respectively, compared to usual care. No significant differences were observed in opioid cessation (Odds ratio: 1.10, 95%CI: -0.48 to 2.67, I2 = 58.59%; two trials) or pain severity (SMD: -0.13, 95%CI: -0.37 to 0.11; I2 = 33.51%; three trials). Adverse events were infrequently examined, with withdrawal symptoms commonly reported. CONCLUSIONS: The studied interventions were effective in reducing opioid dosage for people with CNCP in primary care. They highlighted the importance of multidisciplinary collaboration. Large-scale RCTs measuring the long-term effects and cost of these interventions are needed before their implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.044 |
| Bibliometrics | 0.008 | 0.007 |
| 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) | 0.004 | 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".