The influence of talus size and shape on <i>in vivo</i> talocrural hopping kinematics
Bibliographic record
Abstract
Abstract Talus implants often come in standard sizes and shapes; however, humans vary in their bone size and shape. Consequently, patient-specific implants are becoming available. Understanding how shape changes alter function in a healthy cohort may help designers determine how much specificity is required in talocrural implants. Nine participants (5 females) hopped on one leg while biplanar video radiography and force plate data were collected. 3D bone models were created from computed tomography scans. Helical axes of motion were calculated for the talus relative to the tibia (rotation axes) and a cylinder was fit through the talar dome (morphological axis). Bland-Altman plots and spatial angles tested whether the rotation and morphological axes agree. A shape model of 36 (15 females) participants was established and a cylinder fit was morphed through the range of ±3 standard deviations. The rotation and morphological axes largely agree regarding their orientation and location during hopping. The morphological axis consistently overestimates the orientation-component in anterior-posterior direction. Some shape components affect talar dome orientation and curvature independent of size. This suggests that besides size, the shape of the talar dome might affect the movement pattern during locomotion. Our findings are important to inform talocrural joint arthroplasty design.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".