The Impact of Non-Steroidal Anti-inflammatory Drugs on Postoperative Outcomes in Posterior Spinal Surgery: A Systematic Review
Bibliographic record
Abstract
Study DesignSystematic Review.ObjectiveTo review the effect of NSAID administration on postoperative outcomes following posterior spinal surgery, with specific attention to pain control, opioid reduction, bleeding risk, and fusion outcomes.MethodsPubMed, Google Scholar, Cochrane Library, Embase, Scopus, and Web of Science were queried through September 2025. Studies were included if they included adult patients undergoing posterior spinal procedures, including both fusion and non-fusion surgeries. In addition, studies had to report perioperative NSAID use in posterior spinal surgery with relevant clinical outcomes: Postoperative pain, opioid consumption, blood loss, wound complications, nonunion, and hardware failure. Bias was assessed with MINORS and Newcastle-Ottawa Scales.ResultsEleven studies (396,206 patients) were included. Across posterior fusion and non-fusion procedures, short-term NSAID use consistently reduced postoperative opioid consumption and improved early pain control, with no clinically meaningful increase in bleeding or wound complications. However, prolonged NSAID use was associated with increased risks of nonunion, hardware failure, and wound complications in fusion cohorts. Significant variability in NSAID type, dose, timing, and study design exists.ConclusionsShort-term NSAID use appears safe and effective as an opioid-sparing strategy in non-fusion posterior spinal surgery. Prolonged or high-dose administration may compromise fusion outcomes. Future high-quality studies are needed to definitively define the optimal postoperative NSAID window, dosing, and administration route.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".