Movement Asymmetries: from their Molecular Origin to the Analysis of Movement Asymmetries in Sportsmen
Bibliographic record
Abstract
Asymmetry plays a major role in biology at all scales. This can be seen examining the helix of DNA, the fact that the human heart is on the left side, or that most people use their right hand. A single protein such as Myosin 1D can induce helical motion in another molecule. This causes cells, organs, and even entire bodies to twist in a domino effect, causing left-right behaviour. More in general, athlete movements are often asymmetric and, during the physical rehabilita-tion after injury the asymmetry is visually discernible. Herein we review the molecular basis of movement asymmetries and report on the available knowledge on the few therapeutics inves-tigated so far such as meloxicam. From a more rehabilitative perspective, it is very important to use effective methods to control the process of resolving the injury-related movement asym-metry through the complex use of specialized exercises, measurements and gait analysis which all can provide useful information on the effectiveness of rehabilitation plans. If for each athlete the normal range of asymmetry is known, the asymmetry can be treated individually and the evolution can be monitored over time. Appropriate measures should be taken if the movement asymmetry is outside this range. In addition, genetic, physiological, and psychological factors relevant to athlete health should be considered in the process of assessing and improving exer-cise asymmetry as we also discuss in this review. The main proposal of this work is that move-ment asymmetries in athletes should be treated individually, taking into account the athlete’s genetics, physical condition, and previous 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".