Patient-specific finite element model of the spine and spinal cord to assess the neurological impact of scoliosis correction: preliminary application on two cases with and without intraoperative neurological complications
Bibliographic record
Abstract
Scoliosis is a 3D deformation of the spine and rib cage. For severe cases, surgery with spine instrumentation is required to restore a balanced spine curvature. This surgical procedure may represent a neurological risk for the patient, especially during corrective maneuvers. This study aimed to computationally simulate the surgical instrumentation maneuvers on a patient-specific biomechanical model of the spine and spinal cord to assess and predict potential damage to the spinal cord and spinal nerves. A detailed finite element model (FEM) of the spine and spinal cord of a healthy subject was used as reference geometry. The FEM was personalized to the geometry of the patient using a 3D biplanar radiographic reconstruction technique and 3D dual kriging. Step by step surgical instrumentation maneuvers were simulated in order to assess the neurological risk associated to each maneuver. The surgical simulation methodology implemented was divided into two parts. First, a global multi-body simulation was used to extract the 3D displacement of six vertebral landmarks, which were then introduced as boundary conditions into the personalized FEM in order to reproduce the surgical procedure. The results of the FEM simulation for two cases were compared to published values on spinal cord neurological functional threshold. The efficiency of the reported method was checked considering one patient with neurological complications detected during surgery and one control patient. This comparison study showed that the patient-specific hybrid model reproduced successfully the biomechanics of neurological injury during scoliosis correction maneuvers.
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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) | 0.000 | 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".