Impact of a novel patient-specific, patient-matched Bezier parametric curve rod platform on proximal junction biomechanics in an in silico thoracolumbar instrumented fusion model
Bibliographic record
Abstract
PURPOSE: To evaluate the biomechanical performance of a novel Bezier surface-smoothed transition rod, and to compare it to conventional and stepped rods, focusing on correction capability, spinal stabilization, instrumentation and spinal loading related to risk of proximal junctional kyphosis (PJK). METHODS: A spine finite element model with patient-specific 3D spinal geometry (severe sagittal imbalance from thoracolumbar kyphosis) was used. Surgical instrumentation with five rod types was simulated: (1) constant 6.0 mm diameter, (2) stepped 6.0 mm-5.0 mm diameter, (3) Bezier 6.0 mm-5.5 mm-5.0 mm diameter, (4) constant 5.5 mm diameter, and (5) Bezier 5.5 mm-5.0 mm-4.75 mm diameter. Gravitational forces and flexion movements were simulated to compare load transfer between the spine and instrumentation. RESULTS: All rod configurations achieved equivalent sagittal correction. Load distribution analysis showed that Bezier rods provided smoother load transitions and better offloading of proximal segments compared to constant diameter rods. The highest moment sustained by the segment adjacent to the instrumentation was observed with the constant 6 mm rod (9N.m), while the Bezier 5.5-5-4.75 mm rod showed the lowest moment (7.5Nm), indicating reduced stress of 16% on the upper adjacent vertebrae. Similarly, the Bezier rods were more effective in offloading pedicle screws up to 45% with respect to the stiffer rod construct, potentially reducing the risk of PJK. CONCLUSIONS: The simulation analysis demonstrates Bezier rods offer promising biomechanical benefits particularly in load distribution and stress reduction at adjacent levels of long thoracolumbar instrumentation. Future efforts will focus on clinical validation and optimization of patient-specific designs.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".