Stand-Alone Anchored Spacer vs Anterior Plate Construct in the Management of Adjacent Segment Disease after Anterior Cervical Discectomy and Fusion: A Systematic Review and Meta-Analysis of Comparative Studies
Bibliographic record
Abstract
Study Design Systematic Review and Meta-Analysis. Objectives This study aimed to evaluate the clinical and radiological outcomes of surgically treated adjacent segment disease (ASDis) following ACDF with either anterior plate construct (APC) or stand-alone anchored spacers (SAAS). Method Multiple databases were searched until December 2022 for pertinent studies. The primary outcome was health-related quality of life outcomes [JOA, NDI, and VAS], whereas, the secondary outcomes included operative characteristics [estimated blood loss (EBL) and operative time (OT)], radiological outcomes [C2-C7 Cobb angle, disc height index (DHI) changes, fusion rate], and complications. Results A total of 5 studies were included, comprising 210 patients who had been surgically treated for cervical ASDis. Among them, 113 received APC, and 97 received SAAS. Postoperative dysphagia was significantly higher in the APC group [47% vs 11%, OR = 7.7, 95% CI = 3.1-18.9, P < .05]. Similarly, operative time and blood loss were higher in the APC group compared to the SAAS group; [MD = 16.96, 95% CI = 7.87-26.06, P < .05] and [MD = 5.22, 95% CI = .35 - 10.09, P < .05], respectively. However, there was no difference in the rate of prolonged dysphagia and clinical outcomes in terms of JOA, NDI, and VAS. Furthermore, there was no difference in the radiological parameters including the C2-7 Cobb angle and DHI as well as the fusion rate. Conclusion Our meta-analysis demonstrated that both surgical techniques (APC and SAAS) are effective in treating ASDis. However, with low certainty of the evidence, considering patients are at high risk of dysphagia following revision cervical spine surgery SAAS may be the preferred choice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| 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".