Adjacent Segment Degeneration Versus Disease After Lumbar Spine Fusion for Degenerative Pathology
Bibliographic record
Abstract
STUDY DESIGN: A systematic review. OBJECTIVE: The purpose of this study was to review the published literature to estimate rates and identify risk factors for adjacent segment degeneration (ASDeg) and adjacent segment disease (ASDis) after lumbar fusion. SUMMARY OF BACKGROUND DATA: Arthrodesis remains a common intervention for the surgical treatment of degenerative spinal disease. Clinical studies have demonstrated variability in the rates of adjacent segment pathology after lumbar fusion. METHODS: This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Symptoms of ASDis were distinguished and defined by the need for a revision surgery procedure to address adjacent level pathology. We searched MEDLINE, EMBASE, Cochrane Library, and CINAHL databases. Extracted data included average patient age, average time to follow-up, type of intervention, potential risk factors, and ASDeg and ASDis incidence. Funnel and forest plots were used to describe heterogeneity and meta-regression to estimate pooled incidence of ASDeg and ASDis. RESULTS: A total of 31 articles with 4206 patients were included for analysis. Combining all extractable data, the overall pooled incidence of ASDeg was 5.9% per year (95% CI, 4.8%, 7.2%), and ASDis was 1.8% (95% CI, 1.3%, 2.4%) per year. The incidence of ASDeg is higher with more motion segments. Sex, age, segmental sagittal alignment, fusion methods, and instrumentation were not associated with an increased risk of ASDeg or ASDis. Radiographic ASDeg did not show strong correlation with clinical outcomes. CONCLUSIONS: The prevalence of ASDeg and ASDis has been variably reported in the literature, and fusion length is the factor most significantly associated with adjacent segment pathology. In guiding surgical strategies to avoid adjacent segment pathology, limiting the number of levels fused may have a greater impact than changes in fusion strategies.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".