187 Ketamine and Spinal Fusion Surgery: Does It Increase or Reduce Postoperative Opioid Consumption? A Systematic Review and Meta-Analysis of Randomized Controlled Trial
Bibliographic record
Abstract
INTRODUCTION: Ketamine is a commonly used anesthetic that can also be used for analgesia and sedation. However, none has examined its role in spinal fusion surgery. METHODS: Using the keywords ketamine, spinal fusion surgery, postoperative, and opioid, each reviewer individually carried out a literature search from PubMed, EuroPMC, and Cochrane CENTRAL until May 15, 2021. We only included studies with the adult population, a randomized controlled trial design, English language publications, and reported the key exposure. Additionally, only fully reviewed literature was included. We also excluded non-human literature. The primary endpoint was morphine consumption after 24 and 48 hours of undertaking surgery, whereas secondary endpoints were pain and complications. The pain was analyzed using the visual analogue scale (VAS). We performed a standardized mean difference meta-analysis for postoperative morphine consumption and pain. For complications, we reported the odds ratio as the pooled effect estimate. Each studies quality included was assessed using the Newcastle-Ottawa Scale. RESULTS: We included four notable studies in this study (n = 515). 34.5% male and 65.5% female with the mean age of 45.4 years old. We found that intraoperative ketamine administration were associated with lower postoperative opioid consumption -38.35 mg (95% CI -53.18, -23.52), decreased VAS score -0.83 (95% CI -1.26, -0.39), and lower postoperative complications (OR 0.80, 95% CI 0.60, 1.06). CONCLUSION: Ketamine administration intraoperatively resulted in a considerable reduction in morphine intake, decreased pain, and fewer adverse reactions in spinal fusion surgery patients.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.041 |
| Bibliometrics | 0.007 | 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) | 0.005 | 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".