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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".