Nonfusion Does Not Prevent Adjacent Segment Disease
Bibliographic record
Abstract
STUDY DESIGN: Case series. OBJECTIVE: The aim of this study was to determine the relationship between fusion and adjacent segment disease via Dynesys long-term outcomes. SUMMARY OF BACKGROUND DATA: Dynesys is a dynamic stabilization system meant to improve symptoms by stabilizing the spine without fusion and avoiding the development of adjacent segment disease. However, few studies have evaluated long-term outcomes. METHODS: All patients were operated on with Dynesys from 2006 to 2009 by a single surgeon at a single institution. We prospectively collected 18 variables among the following categories: patient characteristics, comorbidities, surgical indications, and OR variables. We analyzed two primary endpoints: solid fusion on X-ray and clinical adjacent segment disease (ASD) both at 5 years. Secondary endpoints were time to fusion, time to ASD, reoperation, Oswestry disability index (ODI), and visual analogue scale (VAS) leg pain. We conducted a multivariate analysis via the random forest method. Mann-Whitney U test and Fisher exact test were then used to qualify relationship between variables. RESULTS: We had 52 patients to review in the database. Eight had preexisting ASD. Mean follow-up was 92 months (median 87 months). Fifteen had ASD (29%) during follow-up at a mean 45 months (Median 35 months). Nine had a solid fusion (17%), 2 of which also had ASD. Mean time to fusion was 65 months (median 71 months). Differences in improvement of ODI (P = 0.005) and VAS leg pain (P = 0.002) were significant favoring patients without ASD. The multivariate analysis revealed four variables associated with ASD: prior ASD (OR 11.3, P = 0.005), neurological deficit (OR 8.5, P = 0.018), revision OR (OR 8.5, P = 0.018), and multilevel degeneration (OR 0.184, P = 0.026). No variable was associated with fusion. CONCLUSION: Dynesys was associated with a high rate of ASD over long-term follow-up despite maintaining a low fusion rate. Prior ASD was the strongest predictor of progressive ASD. LEVEL OF EVIDENCE: 3.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".