Calculating Individual and Total Muscular Translational Stiffness: A Knee Example
Bibliographic record
Abstract
Research suggests that the knee joint may be dependent on an individual muscle's translational stiffness (KT) of the surrounding musculature to prevent or compensate for ligament tearing. Our primary goal was to develop an equation that calculates KT. We successfully derived such an equation that requires as input: a muscle's coordinates, force, and stiffness acting along its line of action. This equation can also be used to estimate the total joint muscular KT, in three orthogonal axes (AP: anterior-posterior; SI: superior-inferior; ML: medial-lateral), by summating individual muscle KT contributions for each axis. We then compared the estimates of our equation, using a commonly used knee model as input, to experimental data. Our total muscular KT predictions (44.0 N/mm), along the anterior/posterior axis (AP), matched the experimental data (52.2 N/mm) and was well within the expected variability (22.6 N/mm). We then estimated the total and individual muscular KT in two postures (0 deg and 90 deg of knee flexion), with muscles mathematically set to full activation. For both postures, total muscular KT was greatest along the SI-axis. The extensors provided the greatest KT for each posture and axis. Finally, we performed a sensitivity analysis to explore the influence of each input on the equation. It was found that pennation angle had the largest effect on SI KT, while muscle line of action coordinates largely influenced AP and ML muscular KT. This equation can be easily embedded within biomechanical models to calculate the individual and total muscular KT for any joint.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".