Kinematics of Shoulder Injuries in Throwing Sports
Bibliographic record
Abstract
Muscle injury mechanism should be studied to know how to prevent the muscle injuries. The purpose of this article is to construct a protecting model of shoulder injuries based on classical mechanism and kinematics in throwing sports like baseball pitching, badminton smash, volleyball smash, javelin throwing process etc, and then introduce the products design. Firstly, the biomechanics of muscle were analyzed based on Newton's classical mechanics; then a finite element analysis was used to simulate the shoulder muscles protection. Protective parameters were got to make the protective clothes; finally, the protective performance and the comfortableness has been evaluated by ten healthy participants with the mean age, height, body mass were 23.5 years (SD, 1.5), 1.75m (SD, 0.07), 60.5 kg (SD, 9.1), respectively. The throwing test including the baseball pitching, the badminton smash, the process of volleyball smash, and the javelin throwing process. Three protective clothes have been produced and all of them have a protective effect on the muscle while participants were doing the baseball pitching motion, the badminton smashes motion and the volleyball smash. And it has also met the requirement of the human body. But protecting effect each style given was different in different sports motions.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".