Waveform Analysis To Identify Biomechanical Relationships and Differences Between Softball Pitchers With and Without Pain
Bibliographic record
Abstract
BACKGROUND: Softball pitchers accrue high rates of injury. Research suggests certain mechanics at discrete pitch events are related with pain. Here, we examine relationships between peak throwing shoulder kinetics and trunk/pelvis kinematics and compare trunk/pelvis kinematics between pitchers who were healthy and those currently experiencing pain. HYPOTHESIS: (1) Peak shoulder kinetics would be positively related to greater trunk and pelvis flexion, lateral flexion, and rotation; and (2) pitchers in pain would exhibit greater trunk and pelvis flexion, lateral flexion, and rotation during the pitch than those who were pain-free. STUDY DESIGN: Cross-sectional study. METHODS: A total of 42 high school pitchers (height, 1.71 ± 0.06 m; weight, 75.0 ± 15.9 kg; age, 16 ± 2 years) were separated into 2 groups based on presence or absence of pain. Peak kinetic data from 3 pitches per pitcher were averaged and used as dependent variables. Kinematic data were averaged across 3 trials, and time normalized to 101 datapoints between foot contact and follow-through of the pitch. Statistical parametric mapping regressions were used to assess the relationships between peak shoulder kinetics and waveform of trunk and pelvis kinematics. RESULTS: = 0.02). Waveform analyses revealed no waveform differences between healthy pitchers and those currently experiencing pain. CONCLUSION: Peak shoulder kinetic variables are related with pelvic positioning during the pitch; however, trunk and pelvis kinematics do not differ according to presence of pain. CLINICAL RELEVANCE: Pitchers in pain do not adopt specific trunk and pelvic alterations during the pitch, potentially concealing the effects of pain from visual identification. Coaches and clinicians need to discuss health status with pitchers versus relying on visual observations to understand pain and injury risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".