Prediction of the Shape of Severely Fractured Distal Tibia by Using Statistical Shape Modelling
Bibliographic record
Abstract
The design of tibial components of ankle implants is critical for proper functioning.The tibial component design uses the shape of the distal tibial bone as reference.However, when the distal tibia is severely damaged or fractured, the design of tibial components becomes very difficult.In this paper, we aim to study prediction of the distal tibia shape based on the remainder of tibial bone.We use statistical shape modelling technique to create a model of the tibial bone, and then we assess its shape variability.We extract the relationships between the shape variations to produce the predictions.A dataset of 22 female bone samples and 30 male bone samples were acquired.A statistical shape model per gender was produced by using a part of the dataset population.The first set of principal component analysis modes accounted for at least 95% of the shape variations were adopted.For the rest of the bone samples, we attempted to predict their distal tibia shapes by feeding the shapes of their proximal tibia and tibial shafts into the models.The prediction was done for roughly up to 20 mm above the distal tibial articular surface.The study was done in a 5-fold cross validation setting.Root-mean-square errors of reconstruction of the samples excluded in the model development were 1.91 ± 0.61 mm and 2.01 ± 0.39 mm for females and males, respectively.The prediction errors of the distal tibia, when only the shape of the proximal tibia and tibial shafts were known, were in average 1.96 mm for female's bones and 2.46 mm for male's bones.These small errors can show that the distal tibia shape can be reconstructed based on the proximal and shaft shapes.This is a preliminary result bringing new insights into treatment of ankle orthopaedic diseases.It can pave the way for reconstruction of lost distal tibia.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".