Finite Element Analysis of Sacroiliac Joint Fixation under Compression Loads
Bibliographic record
Abstract
BACKGROUND: Sacroiliac joint (SIJ) is a known chronic pain-generator. The last resort of treatment is the arthrodesis. Different implants allow fixation of the joint, but to date there is no tool to analyze their influence on the SIJ biomechanics under physiological loads. The objective was to develop a computational model to biomechanically analyze different parameters of the stable SIJ fixation instrumentation. METHODS: A comprehensive finite element model (FEM) of the pelvis was built with detailed SIJ representation. Bone and sacroiliac joint ligament material properties were calibrated against experimentally acquired load-displacement data of the SIJ. Model evaluation was performed with experimental load-displacement measurements of instrumented cadaveric SIJ. Then six fixation scenarios with one or two implants on one side with two different trajectories (proximal, distal) were simulated and assessed with the FEM under vertical compression loads. RESULTS: The simulated S1 endplate displacement reduction achieved with the fixation devices was within 3% of the experimentally measured data. Under compression loads, the uninstrumented sacrum exhibited mainly a rotation motion (nutation) of 1.38° and 2.80° respectively at 600 N and 1000 N, with a combined relative translation (0.3 mm). The instrumentation with one screw reduced the local displacement within the SIJ by up to 62.5% for the proximal trajectory vs. 15.6% for the distal trajectory. Adding a second implant had no significant additional effect. CONCLUSION: A comprehensive finite element model was developed to assess the biomechanics of SIJ fixation. SIJ devices enable to reduce the motion, mainly rotational, between the sacrum and ilium. Positioning the implant farther from the SIJ instantaneous rotation center was an important factor to reduce the intra-articular displacement. CLINICAL RELEVANCE: Knowledge provided by this biomechanical study enables improvement of SIJ fixation through optimal implant trajectory.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".