Impact of chiropractic care on opioid use for noncancer spine pain: systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Opioids are commonly prescribed for spine-related pain; however, emerging evidence suggests that access to chiropractic care may reduce reliance on opioids. We conducted a systematic review and meta-analysis to assess the impact of chiropractic care on new or continued prescription opioid use among adults with noncancer spine pain. We searched for eligible randomized controlled trials (RCTs) and observational studies in MEDLINE, Embase, AMED, CINAHL, Web of Science, and the Index to Chiropractic Literature up to March 20, 2025. Paired reviewers independently assessed risk-of-bias and extracted data. We performed random- and fixed-effects meta-analyses and used GRADE to assess the certainty of evidence. In total, 2 RCTs (838 participants) and 18 cohort studies (6,035,220 participants) were included in our analyses. We found very low certainty evidence that, compared with standard medical care alone, receipt of chiropractic care may reduce the odds of receiving prescription opioids by 64% (odds ratio [OR] = 0.36; 95% confidence interval [CI], 0.25–0.52; absolute risk reduction [ARR] 15%). However, we found a credible subgroup effect that earlier receipt of chiropractic services (within the first 30 days of presenting with spine-related pain) is associated with a greater decrease in the odds of receiving prescription opioids (OR = 0.33; 95% CI = 0.22–0.51; ARR = 15%) than later (≥30 days after presentation: OR = 0.73; 95% CI = 0.53–0.99; ARR = 8%; test of interaction, P < 0.001), but both with very low certainty evidence. Rigorously designed RCTs are needed to confirm these results.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.048 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".