Which is the most effective treatment for lumbar spinal stenosis: Decompression, fusion, or interspinous process device? A Bayesian network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To compare the clinical efficacy, complications, and reoperation rates among three major treatments for lumbar spinal stenosis (LSS): decompression, fusion, and interspinous process device (IPD), using a Bayesian network meta-analysis. MATERIALS AND METHODS: Databases including Pubmed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science were used for the literature search. Randomized Controlled Trials (RCTs) with three treatment methods were reviewed and included in the study. R software (version 3.6.0), Stata (version 14.0), and Review Manager (version 5.3) were used to perform data analysis. RESULTS: A total of 10 RCTs involving 1254 patients were enrolled in the present study and each study met an acceptable quality according to our quality assessment described later. In direct comparison, IPD exhibited a higher incidence of reoperation than fusion (OR = 2.93, CI: 1.07-8.02). In indirect comparison, the rank of VAS leg (from best to worst) was as follows: IPD (64%) > decompression (25%) > fusion (11%), and the rank of ODI (from best to worst) was: IPD (84%) > fusion (13%) > decompression (4%). IPD had the lowest incidence of complications; the rank of complications (from best to worst) was: IPD (60%) > decompression (27%) > fusion (14%). However, for the rank of reoperation, fusion showed the best results (from best to worst): fusion (79%) > decompression (20%) > IPD (1%). Consistency tests at global and local level showed satisfactory results and heterogeneity tests using loop text indicated a favorable stability. CONCLUSION: The present study preliminarily indicates that non-fusion methods including decompression and IPD are optimal choices for treating LSS, which achieves favorable clinical outcomes. IPD exhibits a low incidence of complications, but its high rate of reoperation should be treated with caution. THE TRANSLATIONAL POTENTIAL OF THIS ARTICLE: For the treatment of LSS, several procedures including decompression, fusion, and IPD have been reported. However, each method has its own advantages and disadvantages. To date, the golden standard treatment for LSS is still controversial. In this network meta-analysis, our results demonstrate that both decompression and IPD obtain satisfactory clinical effects for LSS. IPD is accompanied with a low incidence of complications, however, its high rate of reoperation should be acknowledged with discretion.
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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 | 0.036 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.051 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".