Determination of the Maximal Corrective Ability and Optimal Placement of the Ortho-SUV Frame for Femoral Deformity with respect to the Soft Tissue Envelope, a Biomechanical Modelling Study
Bibliographic record
Abstract
Circular fixation according to the Ilizarov method is a well-recognised modality of treatment for trauma and deformity. One shortcoming of the traditional fixator is its limited ability to correct more than one plane of deformity simultaneously, leading to lengthy frame-time indices. Hexapod circular fixation utilising computer guidance is commonplace for complex multidimensional deformity but difficulties often arise with correction of femoral deformity due to bulkiness of the frame construct, particularly in proximal deformity and in patients of increased size. The Ortho-SUV frame is an innovative hexapod which permits unique customisation to individual patient anatomy to maximise tolerance and optimal range of deformity correction. We hypothesised that the optimal configuration and maximal degree of correction achievable by the Ortho-SUV frame can be biomechanically modelled and applied clinically. A study was constructed using Ortho-SUV and femoral limb models to measure deformity correction via differing frame constructs and determine optimal frame configuration to achieve correction in proximal, middle, and distal third deformities with respect to the soft tissue envelope. The ideal frame configuration is determined for correction of deformity in all locations of the femur with the maximal parameters of correction calculated whilst avoiding and mitigating soft tissue irritation from bulky frame construction.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".