Monolithic superelastic rods with variable flexural stiffness for spinal fusion: Simplified finite element analysis of an instrumented spine segment
Bibliographic record
Abstract
UNLABELLED: Rigid instrumentations have been widely used for spinal fusion but they come with complications, such as adjacent disc degeneration. Dynamic instrumentations have been tested but their efficiency (stabilization capability) and reliability (mechanical integrity of the implant) have yet to be proven. A monolithic Ti-Ni spinal rod with variable flexural stiffness is proposed to reduce the risks associated with spinal fusion while maintaining adequate stabilization. This publication presents a simplified numerical model capable of evaluating the eventual benefits of a Ti-Ni spinal rod with variable flexural stiffness. METHODS: A simplified instrumented spine segment model composed of six vertebrae and five discs has been developed. Two types of spinal rods were evaluated: Classic Ti instrumentation and Ti-Ni rods with variable stiffness. Both instrumentations were tested using two anchor configurations: pedicle screws only or a screws-cable combination. FINDINGS AND DISCUSSION: The all-screws configuration does not allow much motion with either classic Ti or variable Ti-Ni rods. The combination of a Ti rod with screws-cable anchoring allows more motion and, therefore, lower adjacent disk pressure, but puts extremely high stresses on the rod and anchors. The combination of the variable Ti-Ni rod and screws-cable anchoring leads to a significant decrease in adjacent disk pressure, without increasing stresses and pullout forces in the spinal instrumentation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".