Pseudarthrosis risk factors in lumbar fusion: a systematic review and meta-analysis
Bibliographic record
Abstract
This study presents a systematic literature review and meta-analysis of pseudarthrosis risk factors following lumbar fusion procedures. The odds ratio (OR) and 95% confidence interval (95% CI) were used for outcome measurements. The objective of this study was to identify the independent risk factors for pseudarthrosis after lumbar spinal fusion, which is crucial for mitigating morbidity and reoperation. Systematic searches in PubMed, Embase, and Scopus (1990-July 2021) were conducted using specific terms. The inclusion criteria included prospective and retrospective cohorts and case‒control series reporting ORs with 95% CIs from multivariate analysis. The quality assessment utilized the Newcastle-Ottawa scale. Meta-analysis, employing OR and 95% CI, assessed pseudarthrosis risk factors in lumbar fusion surgery, depicted in a forest plot. Of the 568 abstracts identified, 12 met the inclusion criteria (9 retrospective, 2006-2021). The 17 risk factors were categorized into clinical, radiographic, surgical, and bone turnover marker factors. The meta-analysis highlighted two significant clinical risk factors: age (95% CI 1.02-1.11; p = 0.005) and smoking (95% CI 1.68-5.44; p = 0.0002). The sole significant surgical risk factor was the number of fused levels (pooled OR 1.35; 95% CI 1.17-1.55; p < 0.0001). This study identified 17 risk factors for pseudarthrosis after lumbar fusion surgery, emphasizing age, smoking status, and the number of fusion levels. Prospective studies are warranted to explore additional risk factors and assess the impact of surgery and graft type.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".