Biomechanical modelling of indirect decompression in oblique lumbar intervertebral fusions – A finite element study
Bibliographic record
Abstract
BACKGROUND: Oblique lumbar intervertebral fusion aims to decompress spinal nerves via an interbody fusion cage, but the optimal surgical strategy, including implant selection for specific patient characteristics, remains unclear. A biomechanical model was developed to assess how pathophysiological characteristics and instrumentation impact spinal realignment, indirect decompression, and cage subsidence risk. METHODS: A finite element model of the L4-L5 segment was derived from a validated asymptomatic T1-S1 spine model. Five cases of grade I spondylolisthesis with normal or osteoporotic bone densities and initial disc heights of 4.3 to 8.3 mm were simulated. Oblique lumbar intervertebral fusion with cage heights of 10, 12, and 14 mm (12° lordosis) was examined. Postoperative changes in disc height, foraminal and spinal canal dimensions, segmental lordosis, and vertebral slip were assessed. Vertebral stresses and displacements under 10 Nm flexion and 400 N gravitational load were compared between stand-alone constructs and bilateral pedicle screw fixation using rods of 4.75, 5.5, and 6 mm diameters. FINDINGS: Oblique lumbar intervertebral fusion significantly improved postoperative disc height, foraminal and spinal canal dimensions, with the greatest enhancements observed with 14 mm cages. Bilateral pedicle screw fixation markedly reduced cortical endplate stresses and displacements compared to stand-alone constructs, with added benefits from larger rod diameters. Low bone density increased displacements by 63 %. INTERPRETATION: Thicker cages achieve better decompression but increase subsidence risk. Bilateral pedicle screw fixation with 6 mm rods minimizes endplate stresses and displacements, especially in osteoporotic cases. Future research will validate these findings and explore the model's potential for surgical planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".