Biomechanics Related to Increased Softball Pitcher Shoulder Stress: Implications for Injury Prevention
Bibliographic record
Abstract
Background: Softball pitchers exhibit high throwing shoulder distraction force, which is a theorized mechanism of throwing shoulder overuse injury. Windmill pitching involves a variety of highly individualistic pitching styles, and certain kinematics preceding ball release likely influence the amount of shoulder stress that a pitcher accrues. Purpose: To examine the association of trunk and throwing arm kinematics, kinetics, and timing variables with peak throwing shoulder distraction force in high school softball pitchers. Study Design: Descriptive laboratory study. Methods: A total of 37 high school pitchers (mean ± SD; height, 1.71 ± 0.06 m; weight, 75.53 ± 16.12 kg; age, 16 ± 2 years) threw 10 fastball pitches at regulation distance (43 ft [13.1 m]) and with maximum effort. Kinematic data were collected at 240 Hz using an electromagnetic motion capture system synced with motion analysis software. The 3 fastest pitches were averaged and analyzed. Kinematic, kinetic, and timing variables were entered into a stepwise linear regression analysis. Results: Four variables were included in the significant model ( F 4,39 = 147.51; P < .001) and explained peak shoulder distraction force ( R 2 = 0.944; adjusted R 2 = 0.938; SE = 0.036): increased peak elbow distraction force ( t = 19.90; P < .001) and extension moment ( t = 3.63; P = .001), as well as decreased elbow flexion velocity ( t = −2.37; P = .023) and trunk flexion at foot contact of the pitch ( t = −3.00; P = .005). Conclusion: Elbow kinetics and angular velocity, as well as trunk positioning at foot contact, are associated with peak throwing shoulder distraction force in high school softball pitchers. Clinical Relevance: Variables associated with peak throwing shoulder distraction force provide information regarding injury mechanism and coachable cues that might reduce shoulder injury risk among pitchers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".