Efficacy of Proximal Junctional Tethering in Spinal Fusion Surgery for Preventing Proximal Junctional Kyphosis and Proximal Junctional Failure: A Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Spinal fusion surgery is effective for treating various adult spinal deformities. However, spinal fusion surgery is associated with the risk of adjacent segment disease (ASD; 5%-30%), particularly proximal junctional kyphosis (PJK) and proximal junctional failure (PJF). Proximal junctional tethering (PJT) has become a popular technique owing to increasing evidence that it can decrease the rate of PJK or PJF. METHODS: A literature search was conducted using PubMed, Embase, and Cochrane Library. Twelve eligible studies were identified. These studies were predominantly retrospective in nature and compared the incidence of PJK or PJF in adults undergoing spinal fusion surgery with or without PJT. Risk of bias was assessed using the Newcastle-Ottawa scale. All outcomes were analyzed using R software (ver. 4.4.1). RESULTS: We included 8 retrospective cohort studies and 3 propensity-score-matched analyses; these studies comprised 1,424 patients. PJT was associated with a significant decrease in the odds of development of PJK (odds ratio [OR], 0.44; 95% confidence interval [CI], 0.27-0.71) and PJF (OR, 0.36; 95% CI, 0.19-0.69) compared with control. Subgroup analysis results revealed no significant difference in ASD rates between geographical locations, between tethering with and without crosslinks, and between specific tethering techniques. CONCLUSION: PJT significantly reduces the odds of both PJK and PJF in adults undergoing spinal fusion surgery.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.049 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".