Analgesic Safety and Efficacy of Intrathecal Morphine in Elective Posterior Lumbar Fusion: A Systematic Review With Meta-Analyses
Bibliographic record
Abstract
Background. We evaluated perioperative intrathecal morphine (ITM) compared with placebo or standard postoperative pain strategies in adults undergoing elective posterior lumbar fusion. Methods. A systematic search of EMBASE, MEDLINE, the Cochrane Library, and Google Scholar (December 12, 2023) identified studies involving ITM in posterior lumbar fusion. Eligible patients were ≥18 years. Two reviewers extracted outcomes and assessed non-randomized studies with the Newcastle- Ottawa scale. Odds ratios (OR) and mean differences (MD) with 95% confidence intervals (CI) were calculated using fixed- and random-effects models. Results. Eleven studies met the inclusion criteria. ITM significantly lowered opioid requirements within 24 hours (MD -0.72, 95% CI [-1.30, -0.14], I² = 91.08%, p = 0.015) and pain scores at 24 hours (MD -0.57, 95% CI [-1.01, -0.13], I² = 73.17%, p = 0.010). Reductions were also seen in pain scores at 48 hours (MD -0.63, 95% CI [-1.55, 0.29], p = 0.178), hospital stay (MD -0.71, 95% CI [-1.77, 0.36], p = 0.191), sedation (OR -0.18, p = 0.594), and respiratory depression (OR -0.08, p = 0.782), though these were not statistically significant. ITM increased the incidence of pruritus (OR 1.04, 95% CI [0.45, 1.64], I² = 43.55%, p < 0.001), with non-significant rises in urinary retention (OR 0.45, p = 0.070) and nausea/vomiting (OR 0.03, p = 0.847). Conclusion. Although based on relatively small and methodologically heterogeneous studies, these analyses suggest that perioperative ITM enhances analgesia and maintains an acceptable safety profile following posterior lumbar fusion.
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 | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".