Load Transfer Characteristics Between Posterior Spinal Implants and the Lumbar Spine Under Anterior Shear Loading
Bibliographic record
Abstract
STUDY DESIGN: A biomechanical human cadaveric study. OBJECTIVE: To determine the percentage of shear force supported by posterior lumbar spinal devices of varying stiffnesses under anterior shear loading in a degenerative spondylolisthesis model. SUMMARY OF BACKGROUND DATA: Clinical studies have demonstrated beneficial results of posterior arthrodesis for the treatment of degenerative spinal conditions with instability. Novel spinal implants are designed to correct and maintain spinal alignment, share load with the spine, and minimize adjacent level stresses. The optimal stiffness of these spinal systems is unknown. To our knowledge, low-stiffness posterior instrumentation has not been tested under an anterior shear force, a highly relevant force to be neutralized in the clinical case of degenerative spondylolisthesis. METHODS: The effects of implant stiffness and specimen condition on implant load and intervertebral motion were assessed in a biomechanical study. Fifteen human cadaveric lumbar functional spinal units were tested under a static 300 N axial compression force and a cyclic anterior shear force (5-250 N). Implants (high-stiffness [HSI]: ø 5.5-mm titanium, medium-stiffness [MSI]: ø 6.35 × 7.2-mm oblong PEEK, and low-stiffness [LSI]: ø 5.5-mm round PEEK) instrumented with strain gauges were used to calculate loads and were tested in each of 3 specimen conditions simulating degenerative changes: intact, facet instability, and disc instability. Intervertebral motions were measured with a motion capture system. RESULTS: As predicted, implants supported a significantly greater shear force as the specimen was progressively destabilized. Mean implant loads as a percent of the applied shear force in order of increasing specimen destabilization for the HSI were 43%, 67%, and 76%; mean implant loads for the MSI were 32%, 56%, and 77%; and mean implant loads for the LSI were 18%, 35%, and 50%. Anterior translations increased with decreasing implant stiffness and increasing specimen destabilization. CONCLUSION: Implant shear stiffness significantly affected the load sharing between the implant and the natural spine in anterior shear ex vivo. Low-stiffness implants transferred significantly greater loads to the spine. This study supports the importance of load-sharing behavior when designing new implants.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".