A Systematic Review and Meta-Analysis of Silicon Nitride and Biomaterial Modulus as it Relates to Subsidence Risk in Spinal Fusion Surgery
Bibliographic record
Abstract
Introduction: For decades, researchers and surgeons have sought to determine the optimal biomaterial for spinal fusion implants. Successful fusion is associated with improved quality of life while failures are often associated with costly and complex revisions. One common failure is subsidence. Biomaterials with higher modulus are thought to be related to subsidence risk but this has not been thoroughly investigated. The aim of this systematic review and meta-analysis is to assess silicon nitride and biomaterial modulus as they relate to subsidence risk in spinal fusions. Methods: A systematic review was conducted using the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines. Databases searched included PubMed-Medline, Google Scholar, Embase, EBSCO, and Cochrane Library. Study quality was assessed according to the Newcastle-Ottawa Scale. A network meta-analysis was chosen, allowing for direct and indirect comparisons for multiple treatments using a Bayesian hierarchical framework with Markov chain Monte Carlo methods. Outcomes were reported as odds ratios with 95% confidence intervals. Heterogeneity between studies was evaluated using the I 2 test. A pairwise meta-analysis was also produced to compare the results of network analysis for consistency. Publication bias was assessed using a funnel plot, Egger test, and Begg test. All analyses were conducted using R (Project for Statistical Computing, ver. 4.0.4). Results: The initial search yielded a total of 821 articles. After removal of duplicates and screening based on inclusion and exclusion criteria, 64 articles were available for review and 13 were selected for meta-analysis. Biomaterial implant types in the final studies included: silicon nitride (Si 3 N 4 ), polyetheretherketone (PEEK), titanium (Ti), and two composites, nano-hydroxyapatite/polyamide 66 (n-HA/PA66) and a carbon fiber reinforced polymer (CFRP). A total of 1,192 patients were included in this analysis -419 with titanium implants, 460 with PEEK, 96 with Si 3 N 4 , 332 with n-HA/PA66, and 35 with CFRP. Titanium had the highest rate of subsidence compared to other biomaterials. Pairwise analysis was consistent with these results. Both the Egger test (p = 0.28) and Begg test (p = 0.37) were found to be non-significant for publication bias. Conclusions: Spinal fusion implants derived from Si 3 N 4 , compared to PEEK and titanium, do not appear to be correlated with increased subsidence risk. sensible and economic. Improved healing and adequate fusion are associated with improved quality of life, faster return to work, less pain, less opiate use, and fewer repeat surgeries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".