Static Versus Expandable Cages in Minimally Invasive Lateral Lumbar Interbody Fusion
Bibliographic record
Abstract
STUDY DESIGN: A meta-analysis approach to a systematic review. OBJECTIVE: Perform a systematic review to identify all reports directly comparing outcomes of lateral lumbar interbody fusion (LLIF) using static versus expandable interbody cages. Specifically focusing on periprocedural complications, intraoperative morbidity, and fusion outcomes. SUMMARY OF BACKGROUND DATA: Minimally invasive surgical techniques, particularly LLIF, have gained popularity for their potential to reduce muscle and soft tissue dissection, leading to faster postoperative recovery. LLIF has been associated with fewer complications compared with open posterior approaches. The introduction of expandable lumbar interbody devices aims to further reduce surgical difficulty and potential complications. However, concerns include a small graft window due to the expansion mechanism and higher costs. METHODS: The Web of Science, Scopus, and PubMed databases were systematically queried in accordance with PRISMA guidelines to identify articles comparing outcomes following LLIF using static and expandable interbodies. The Newcastle-Ottawa Scale (NOS) was employed to assess the risk of bias (ROB) in the selected studies. Extracted data underwent effect-size meta-analysis with the PyMARE library, using P <0.05 to define statistical significance. RESULTS: Of the 77 identified articles, 4 studies comprising 283 patients (mean age: 67.1 y, 55.8% female) met the inclusion and exclusion criteria. A total of 150 patients (53%) were treated with static interbodies compared with 133 (47%) receiving expandable interbodies. The groups did not differ significantly with respect to operative time ( P =0.59), blood loss ( P =0.89), length of stay ( P =0.78), subsidence ( P =0.49), 24-month mean disc height ( P =0.11), 24-month mean ODI ( P =0.58), or 24-month mean visual analog scale (VAS) back pain ( P =0.81). The expandable group saw a trend toward improved fusion rates (97% vs. 92%, P =0.06). CONCLUSIONS: The present meta-analysis suggests the use of expandable (vs. static) interbodies in LLIF surgery may result in similar surgical morbidity, subsidence, and decompression. Further prospective comparative studies are merited to validate these results.
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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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.035 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".