Internal Tibial Forces and Moments During Graded Running
Bibliographic record
Abstract
The stress experienced by the tibia has contributions from the forces and moments acting on the tibia. We sought to quantify the influence of running grade on internal tibial forces and moments. Seventeen participants ran at 3.33 m/s on an instrumented treadmill at 0 deg, ±5 deg, and ±10 deg while motion data were captured. Ankle joint contact force was estimated from an anthropometrically-scaled musculoskeletal model using inverse dynamics-based static optimization. Internal tibial forces and moments were quantified at the distal 1/3rd of the tibia, by ensuring static equilibrium with all applied forces and moments. Downhill running conditions resulted in lower peak internal axial force (range of mean differences: -9% to -16%, p < 0.001), lower peak internal anteroposterior force (-14% to -21%, p < 0.001), and lower peak internal mediolateral force (-14% to -15%, p < 0.001), compared to 0 deg and +5 deg. Furthermore, downhill conditions resulted in lower peak internal mediolateral moment (-11%to -21%, p < 0.001), lower peak internal anteroposterior moment (-13% to -14%, p < 0.001), and lower peak internal torsional moment (-9% to -21%, p < 0.001), compared to 0 deg, +5 deg, and +10 deg. The +10 deg condition resulted in lower peak internal axial force (-7% to -9%, p < 0.001) and lower peak internal mediolateral force (-9%, p = 0.004), compared to 0 deg and +5 deg. These findings suggest that downhill running may be associated with lower tibial stresses than either level or uphill running.
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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 | 0.001 |
| 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) | 0.001 | 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".