3D Digitizing Device Applied in Evaluation and Simulation of Postoperative Trunk Surface Shape in Adolescent Idiopathic Scoliosis
Bibliographic record
Abstract
Adolescent idiopathic scoliosis (AIS) is a complex 3D deformation of the musculo-skeletal system of the trunk, with a prevalence of about 1% to 3% in the general population.Scoliosis is clinically apparent by observing the asymmetry of spinous processes, ribs, and scapulae, imbalance between the top and bottom of the spine, and left-right asymmetry of the trunk.Among patients with AIS, about 1 in 1,000 will need surgery using spinal instrumentation and fusion to correct the deformity.However, while the surgeon's main goals are to correct the spinal deformity and achieve spinal balance, the most important outcome for patients is the correction of the external shape of the trunk.This paper provides an overview of work done in recent years by our research group to exploit data collected using a Creaform surface digitizing setup to study the surgical correction of trunk external shape of AIS patients treated at Sainte-Justine University Hospital Center (CHU).We first describe our surface acquisition system and clinical setup.We then introduce a set of clinical measurements (indices) based on the trunk's external shape, to quantify its degree of asymmetry.We then present the results of a preliminary study assessing the effect of scoliosis surgery on the external trunk shape.We finally present a hybrid, deformable model of the human trunk for prediction of surgical outcome on trunk shape in AIS.The longer term aim of this research is to develop a validated simulation tool that would allow the clinician to illustrate to the patient the potential result of the surgery and would help in deciding on a surgical strategy that could most improve their external appearance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".