Functional relevance of the small muscles crossing the ankle joint – the bottom-up approach
Bibliographic record
Abstract
It has been suggested that increasing muscle strength could help reducing the frequency of running injuries and that a top-down approach using an increase in hip muscle strength will result in a reduced range of movement and reduced external moments at the knee and ankle level. This paper suggests, that a bottom-up approach using an increase of strength of the small muscles crossing the ankle joint, should reduce movement and loading at the ankle, knee and hip. This bottom-up approach is discussed in detail in this paper from a conceptional point of view. The ankle joint has two relatively “large” extrinsic muscles and seven relatively small extrinsic muscles. The large muscles have large levers for plantar-dorsi flexion but small levers for pro-supination. In the absence of strong small muscles the large muscles are loaded substantially when providing balancing with respect to pro-supination. Specifically, the Achilles tendon will be loaded in this situation asymmetrically with high local stresses. Furthermore, a mechanical model with springs shows that (a) the amplitude of the displacement with the strong small springs is smaller and (b) that the loading in the joints of the springs is substantially smaller for the model with the strong small springs. Additionally, strong and active small muscles crossing the ankle joint provide stability for the ankle joint (base). If they are weak, forces in the ankle, knee and hip joint increase substantially due to multiple co-contractions at the joints. Finally, movement transfer between foot and tibia is high for movements induced from the bottom and small for movements induced from the top. Based on these considerations one should speculate that the bottom-up approach may be substantially more effective in preventing running injuries than the top down approach. Various possible strategies to strengthen the small muscles of the ankle joint are presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".