Preoperative Planning Simulator for Spinal Deformity Surgeries
Bibliographic record
Abstract
In Brief Study Design. Proof of concept of a spine surgery simulator (S3) for the assessment of scoliosis instrumentation configuration strategies. Objective. To develop and assess a surgeon-friendly spine surgery simulator that predicts the correction of a scoliotic spine as a function of the patient characteristics and instrumentation variables. Summary of Background Data. There is currently no clinical tool sufficiently user-friendly, reliable and refined for the preoperative planning and prediction of correction using different instrumentation configurations. Methods. A kinetic model using flexible mechanisms has been developed to represent patient-specific spine geometry and flexibility, and to simulate individual substeps of correction with an instrumentation system. The surgeon-friendly simulator interface allows interactive specification of the instrumentation components, surgical correction maneuvers and display of simulation results. Results. The simulations of spinal instrumentation procedures of 10 scoliotic cases agreed well with postoperative results and the expected behavior of the instrumented spine (average Cobb angle differences of 3.5° to 4.6° in the frontal plane and of 3.6° to 4.7° in the sagittal plane). Forces generated at the implant-vertebra link were mostly below reported pull-out values, with more important values at the extremities of the instrumentation. Conclusion. The spine surgery simulator S3 has proven its technical feasibility and clinical relevance to assist in the preoperative planning of instrumentation strategies for the correction of scoliotic deformities. A spine surgery simulator (S3) has been developed and tested using 10 adolescents with idiopathic scoliosis that underwent corrective surgery. This software allows surgeons to perform the different steps of a corrective instrumentation surgery on a personalized three-dimensional biomechanical model of the patient and provides animated results of the simulated correction. This study is a proof of concept of its technical feasibility and clinical relevance.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".