Muscle tuning and preferred movement path – a paradigm shift
Bibliographic record
Abstract
In the last 40 years, the scientific debate around running injuries and running shoes has been dominated by two paradigms, the ‘impact’ and the ‘pronation’ paradigms. However, the development of running shoe technologies aimed at reducing impact forces and pronation has not led to a decline of running-related injuries. This article recommends to abandon the ‘impact’ and ‘pronation’ paradigms due to a lack of biomechanical and epidemiological evidence and instead suggests a shift to new paradigms: ‘Muscle tuning’ and the ‘preferred movement path’. These paradigms represent new approaches to understanding the biomechanical patterns of each individual runner and how they are controlled by the neuromuscular system. Experimental evidence in support of the ‘muscle tuning’ and ‘preferred movement path’ paradigms are presented and discussed regarding their relevance for running performance, injuries, and footwear. Finally, this paper proposes that the concept of ‘functional groups’ should be used and further developed to overcome the challenge that groups of individuals respond differently to footwear interventions. First, groups of individuals who behave similarly (functional groups) should be identified. Second, running shoes should be selected to match the characteristics of the identified functional groups in order to optimize the beneficial effects of running shoes for improving running performance and reducing the risk of running injuries.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.017 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".