Biomechanical Impact of Splint Rods in Posterior Cervicothoracic Fixation: A Finite Element Analysis
Bibliographic record
Abstract
Study DesignBasic Science Study.ObjectiveTo determine the impact of splint rods, rod material, and rod diameter on cervicothoracic construct load bearing capacity.MethodsFinite element analysis was used to simulate 8 construct variations in a C7 vertebrectomy model with pedicle screw fixation in C5-T2. Variations included the material of rods (titanium or cobalt-chrome alloys), presence or absence of splint rods, the diameters of the splint rods (3.5 or 4.5 mm), and the size of lateral mass screws (3.5 mm or 4.0 mm). Boundary conditions replicated ASTM F1717/ISO 12189 standards. Yield load, displacement, stiffness, and stress distributions were analyzed under worst-case loading (2 cm displacement).ResultsThe best configuration was comprised of 3.5 mm titanium primary and splint rods, which achieved the highest load capacity of 107N and stiffness of 29.8 N/mm. The worst configuration was comprised of single 3.5 mm titanium rods, which demonstrated the lowest load capacity of 66N.ConclusionsAdding splint rods improves load bearing capacity of cervicothoracic fixation constructs. Increasing construct stiffness through increasing diameter of the rods or screws, or through change in rod alloy to cobalt chrome does not always result in an improvement of the load bearing capacity as it risks earlier failure at the bone-screw interface. Optimal construct design is a careful balance between construct stiffness and load-sharing.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".