Comparison of long-term outcomes of spinal fusion surgeries supplemented with “topping-off” implants in lumbar degenerative diseases: A systematic review and network meta-analysis
Bibliographic record
Abstract
Background Context Spinal fusion surgery is a common treatment for lumbar degenerative diseases and has been associated with the long-term complication of adjacent segment disease (ASD). In recent years, the "topping-off" technique has emerged as a new surgical method, combining spinal fusion with a hybrid stabilization device (HSD) or interspinous process device (IPD) proximal to the fused vertebrae. Methods A literature search using the PubMed, Cochrane Central Register of Controlled Trials, EMBASE, and Web of Science databases identified eligible studies comparing topping-off implant(s) with spinal fusion surgery for lumbar degenerative diseases. Risk of bias was assessed using the Cochrane RoB 2.0 tool for randomized controlled trials and the Newcastle–Ottawa scale for retrospective studies. Each outcome was analyzed using the statistical Confidence in NMA (CINeMA) 1.9.0 software. Results 17 RCTs and retrospective studies that included 1255 participants and five interventions were identified. The topping-off implants device for intervertebral assisted motion (DIAM; OR = 0.235, p < 0.001), Dynesys (OR = 0.413, p < 0.001), and Coflex (OR = 0.417, p < 0.01) significantly lowered the incidence of radiographic adjacent segment degeneration (RASDeg) compared with spinal fusion surgery alone. Spinal fusion supplemented with DIAM significantly reduced the incidence of clinical adjacent segment disease (CASD) (OR = 0.358, p = 0.032). Conclusions Spinal fusion supplemented with DIAM substantially reduced the incidence of radiographic and clinical adjacent segment disease. No significant difference was observed between the treatment comparators for reoperation due to ASD and back pain relief score.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| 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".