Decompression with interbody fusion versus decompression alone for degenerative lumbar diseases: A meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To appraise the clinical effectiveness and complications of two surgical approaches, namely decompression alone (DA) versus decompression with interbody fusion (DF), in managing degenerative lumbar diseases (DLD). METHODS: As of July 1, 2024, an exhaustive search identified all randomized controlled studies and cohort studies comparing DA and DF in DLD management. Relevant data were extracted using strict criteria, and study quality was assessed via the Newcastle-Ottawa Scale and Cochrane Collaboration's tool. The extracted outcomes encompassed a range of measures, including operative duration, intraoperative hemorrhage, hospitalization length, time to ambulation, short form 12 physical component score (SF12-PCS), low back pain visual analog scale (VAS) score, leg pain VAS score, Oswestry disability index (ODI), Japanese orthopedic association (JOA) score, EuroQol five dimensions (EQ-5D), incidence of complications, reoperation rate, and Odom's criteria. RESULTS: A total of 35 articles were included in this study, involving 12,030 patients. Of these, 7,442 patients were in the DA group, while 4,588 were in the DF group. Operative duration was shorter (MD = -89.09, 95%CI -92.71, -85.47, P < 0.00001), intraoperative hemorrhage was less (MD = -242.26, 95%CI -252.16, -232.36, P < 0.00001), hospitalization length was shorter (MD = -2.36, 95%CI -2.59, -2.14, P < 0.00001), and time to ambulation was reduced (MD = -10.49, 95%CI -12.52, -8.46, P < 0.00001) in the DA group than in the DF group. At the final follow-up for ODI, the DF group demonstrated statistically superior outcomes compared to the DA group (MD = 1.28, 95%CI 0.35, 2.21, P = 0.007). Data revealed no significant differences in SF12-PCS, JOA score, back pain VAS score, leg pain VAS score, final follow-up EQ-5D, reoperation rates, complication rates, and Odom's criteria (P > 0.05). CONCLUSION: When treating DLD, DA offers more favorable outcomes in terms of operative duration, intraoperative hemorrhage, hospitalization length, and time to ambulation. These findings suggest that DA should be considered the preferred surgical approach for most DLD patients, unless specific indications for fusion exist. Clinicians should tailor decisions to each surgery's specifics to optimize patient outcomes. TRIAL REGISTRATION: PROSPERO registration number: CRD42024580975.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.055 |
| Bibliometrics | 0.006 | 0.005 |
| 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) | 0.004 | 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".