Comparing Outcomes of Banana-Shaped and Straight Cages in Transforaminal Lumbar Interbody Fusion for Lumbar Degenerative Diseases: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: This meta-analysis aims to refine the understanding of the optimal choice between different cage shapes in transforaminal lumbar interbody fusion (TLIF) by systematically comparing perioperative data, radiological outcomes, clinical results, and complications associated with banana-shaped and straight bullet cages. METHODS: A meticulous literature search encompassing PubMed, Embase, Scopus, Web of Science, China Knowledge Network, and Wanfang Data was executed up to October 5, 2023. Inclusion criteria focused on studies comparing banana-shaped and straight bullet cages in TLIF. The quality of included studies was assessed using appropriate tools such as the Newcastle-Ottawa Scale (NOS) for nonrandomized studies. Rigorous evaluations were performed for radiographic outcomes, including disc height (DH), segmental lordosis (SL), lumbar lordosis (LL), subsidence, and fusion rates. Clinical outcomes were meticulously evaluated using visual analogue scale (VAS), Oswestry Disability Index (ODI), and complications. RESULTS: The analysis incorporated 7 studies, involving 573 patients (297 with banana-shaped cages, 276 with straight cages), all with NOS ratings exceeding 5 stars. No statistically significant differences were observed in operative time, blood loss, or hospitalization between the 2 cage shapes. Banana-shaped cages exhibited greater changes in DH (p = 0.001), SL (p = 0.02), and LL (p = 0.01). Despite statistically higher changes in ODI for straight cages (26.33, p < 0.0001), the actual value remained similar to banana-shaped cages (26.15). Both cage types demonstrated similar efficacy in VAS, complication rates, subsidence, and fusion rates. CONCLUSION: Although banana-shaped cages can excel in restoring DH, SL, and LL, straight bullet cages can provide comparable functional improvements, pain relief, and complication rates.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.007 | 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) | 0.002 | 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".