Association between sagittal balance and adjacent segment degeneration in anterior cervical surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: ASD is a relatively common degenerative alteration after cervical surgery which occurs above or below the fused segment. In addition, some patients may need reoperation to treat severe ASD after the primary surgery. It was considered that sagittal balance is correlated with postoperative clinical outcomes; however, few studies have reported the influence of sagittal balance on ASD. The present study is designed to investigate whether sagittal balance impacts the pathology of adjacent segment disease (ASD) in patients who undergo anterior cervical surgery for degenerative cervical disease. METHODS: Databases including Pubmed, Embase, Cochrane library, and Web of Science were used to search for literature published before June 2018. Review Manager 5.3 was used to perform the statistical analysis. Sagittal balance parameters before and after surgery were compared between patients with and without ASD. Weighted mean difference (WMD) was summarized for continuous data and P < 0.05 was set for the level of significance. RESULTS: A total of 221 patients with ASD and 680 patients without ASD from seven articles were studied in this meta-analysis. There were no significant differences in most sagittal balance parameters between the two groups, except for postoperative cervical lordosis (CL) (WMD -3.30, CI -5.91, - 0.69, P = 0.01). CONCLUSIONS: Some sagittal balance parameters may be associated with the development of ASD after anterior cervical surgery. Sufficient restoration of CL may decrease the incidence of ASD. The results in present study needed to be expanded carefully and further high-quality studies are warranted to investigate the impact of sagittal balance on ASD.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".