Human <i>in vivo</i> midtarsal and subtalar joint kinematics during walking, running, and hopping
Bibliographic record
Abstract
Abstract The interaction among joints of the midtarsal complex and subtalar joint is essential role for locomotor function; however, its complexity poses substantial challenges in quantifying their motions. We determine the mobility of these joints across locomotion tasks and investigate their alignment with individual talus morphology. Utilizing highly accurate biplanar videoradiography, three-dimensional bone kinematics were captured during walking, running, and hopping. We calculated the axis of rotation of each midtarsal and subtalar joint for the landing and push-off phases, respectively. A comparison was made between these rotation axes and the morphological subtalar axis. Measurement included total rotation about, the orientation of the rotation axes in the direction of the subtalar joint and its deviation via spatial angles for both phases. The rotation axes of all three bones relative to the talus closely align with the morphological subtalar axis. This suggests that the midtarsal and subtalar joints’ motions might be described by one commonly oriented axis. Despite having such axis, the location of axes and ranges of motion differed among the bones. Our results provide a novel perspective of healthy foot function across different sagittal plane-dominant locomotion tasks underscoring the importance of midtarsal and subtalar motion with respect to subject-specific talus morphology.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".