The safety and efficacy of anti-inflammatory-impregnated gelatin sponge in spine surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
The purpose of this systematic review and meta-analysis is to evaluate the safety and efficacy of anti-inflammatory-impregnated gelatin sponges in spine surgeries. Gelatin sponges are increasingly used as delivery vehicles for anti-inflammatory and analgesic drugs during spine surgeries. However, concerns about their safety and efficacy persist. A comprehensive literature search was conducted to identify original research articles investigating the use of anti-inflammatory-impregnated gelatin sponges in spine surgeries from 2006 to 2024. Case reports, case series, animal studies, cadaveric studies, and abstract-only articles were excluded. The risk of bias was assessed using Cochrane Risk of Bias 2.0 (Cochrane, UK) for randomized controlled trials (RCTs) and the Newcastle Ottawa Scale (NOS) for observational studies. Meta-analysis was performed using Cochrane Review Manager Web. Thirteen studies (six RCTs, six cohort studies, and one case-control study) were included. Pooled analysis revealed a significant decrease in Visual Analog Scale (VAS) score for back pain (mean difference [MD], -0.62; 95% confidence intervals [CI], -0.78 to -0.46; p<0.00001), VAS score for leg pain (MD, -0.60; 95% CI, -0.87 to -0.34; p<0.00001), and length of hospital stay (MD, -0.99; 95% CI, -1.68 to -0.31; p=0.0004). Additionally, there was a significant increase in the Japanese Orthopedic Association score (MD, 0.98; 95% CI, 0.00 to 1.96; p=0.05). However, no significant difference was observed in the disability index (MD, -0.59; 95% CI, -1.88 to -0.70; p=0.37). The use of anti-inflammatory-impregnated gelatin sponges during spine surgeries decreases postoperative back pain and leg pain, reduces length of stay, and improves neurological function. Larger, prospective, randomized trials are required to obtain more robust evidence.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.046 |
| Bibliometrics | 0.009 | 0.009 |
| 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) | 0.003 | 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".