Shear force measurements on low- and high-stiffness posterior fusion devices
Bibliographic record
Abstract
Low-stiffness posterior fusion devices for the lumbar spine have been developed to treat degenerative spinal conditions. However, the demands on an implant vary between a stable motion segment and one which exhibits a significant degree of sagittal plane instability. Shear motion in the antero-posterior direction is a relevant mode of instability for clinical conditions such as degenerative lumbar spondylolisthesis. Shear load-sharing between the implant and spine in conditions of antero-posterior instability has not been studied, nor have there been comparisons between traditional rigid implants and novel low-stiffness implants. The objective of this study was to develop a method to measure in vitro shear forces on three clinically relevant fusion implants when they are applied to an unstable model of degenerative spondylolisthesis in a human cadaver spine. Uniaxial strain gauges were affixed to the surface of the implants and a spine-segment-specific calibration method was used to calibrate the strain output to an applied shear force. The accuracy of the force measurements was within 3.4N for all implant types and the repeatability was within 5.4N. The force measurement technique was sufficiently accurate and reliable to conclude that it is suitable for use in in vitro experiments to measure implant shear force.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".