Optimized Use of Multi-Functional Positioning Frame Features for Scoliosis Surgeries
Bibliographic record
Abstract
A multi-functional positioning frame (MFPF) has been developed which includes a number of positioning features allowing for hip flexion and extension, thorax vertical displacement, lateral leg displacement, pelvic torsion and thorax lateral displacement. The objective of this study was to develop a method allowing for optimized combined use of the MFPF features. Finite element models (FEMs) representing the osseo-ligamentous structures of the spine, ribcage, pelvis and lower limbs, including muscles, were created for three different curve types (main thoracic, double major, and triple major) using a radiographic bi-planar reconstruction technique. Each FEM was subjected to an experimental design in which MFPF features were independently and simultaneously varied between extreme positions and the resultant changes in spinal geometry measured. Optimization of individual spinal geometrical parameters showed variability between curve types and some patterns such as minimum Cobb with lower limbs displaced laterally towards the convexity, pelvis raised on the side of concavity, and thorax laterally displaced towards the thoracic concavity. A weighted and normalized global optimization equation was developed which accounts for the relative importance and desired values of each geometrical parameter. Combined use of MFPF features and adjustments offers a wider range of possible intra-operative spinal geometries than their individual use.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".