Do the Choice of Fusion Construct With and Without Autograft Influence the Fusion and Complication Rates in Patients Undergoing 1 or 2-Level Anterior Cervical Discectomy and Fusion Surgery? A PRISMA-Compliant Network Meta-Analysis
Bibliographic record
Abstract
STUDY DESIGN: Network meta-analysis. OBJECTIVES: To compare the fusion outcome and complications of different 1 or 2-level anterior cervical decompression and fusion (ACDF) constructs performed with and without the application of autografts. METHODS: We performed an independent and duplicate search in electronic databases including PubMed, Embase, Web of Science, Cochrane, and Scopus for relevant articles published between 2000 and 2020. We included comparative studies reporting fusion rate and complications with and without the use of autografts in ACDF across 5 different fusion constructs. A network meta-analysis was performed in Stata, categorized based on the type of fusion constructs utilized. Fusion constructs were ranked based on p-score approach and surface under cumulative ranking curve (SUCRA) scores. The confidence of results from the analysis was appraised with Cochrane's CINeMA approach. RESULTS: A total of 2216 patients from 22-studies including 6 Randomized Controlled Trials (RCTs) and 16 non-RCTs were included in network analysis. The mean age of included patients was 49.3 (±3.62) years. Based on our meta-analysis, we could conclude that use of autograft in 1- or 2-level ACDF did not affect the fusion and mechanical implant-related complications. The final fusion and mechanical complication rates were also not significantly different across the different fusion constructs. The use of plated constructs was associated with a significant increase in post-ACDF dysphagia rates [OR 3.42; 95%CI (.01,2.45)], as compared to stand-alone constructs analysed. CONCLUSION: The choice of fusion constructs and use of autografts does not significantly affect the fusion and overall complication rates following 1 or 2-level ACDF 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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.059 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".