A parametric analysis of interbody fusion cages placement: A finite elements approach comparing lumbar lordosis of bullet and steerable banana cages
Bibliographic record
Abstract
Improper cage placement during spinal interbody fusion surgeries could lead to numerous post-operative complications. Biomechanical factors of such improper placement may result in loss of lumbar lordosis, foraminal stenosis, subsidence, and altered stress distribution to the tissues adjacent to the cage. The aim of the present study is to compare three different lumbar interbody cage designs, placed posterior, middle, and anterior, and their biomechanical effect on the aforementioned studied parameter. Cages and MRI-based lumbar spine models were developed using finite elements. A parametric comparative analysis was then designed to explore cage types, height, and location on lumbar lordosis, foraminal area, cage subsidence, along with normal and shear stresses resulting from each cage configuration under a 500 N compression load. First, the model was validated in light of published data. Simulated results showed that lumbar lordosis and foraminal area are inversely related. The 6-degrees bullet cage showed the highest gain in lordosis (16.5 o ), while it exhibited a large loss in foraminal area (34.2 mm 2 ). Anterior placement of banana cages, however, showed the best trade-off, effectively recording a 14.5 o lordosis gain, a 0.6 mm 2 loss in foraminal area, a subsidence as low as 0.27 mm, and a moderate cage stress of 13.6-23.1 MPa. Reported data favors banana cages for the highest lordosis gains without compromising the other explored biomechanical factors. However, it is still advised to thoroughly consider patient-specific factors at hand, possible complications of foraminal stenosis, cage migration, and endplates wear prior to choosing an appropriate cage morphology and placement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".