Baseball Pitching Arm Three-Dimensional Inertial Parameter Calculations From Body Composition Imaging and a Novel Overweight Measure for Youth Pitching Arm Kinetics
Bibliographic record
Abstract
Many baseball pitching studies have used inverse dynamics to assess throwing arm kinetics as high and repetitive kinetics are thought to be linked to pitching injuries. However, prior studies have not used participant-specific body segment inertial parameters (BSIPs), which are thought to improve analysis of high-acceleration motions and overweight participants. This study's objectives were to (1) calculate participant-specific BSIPs using dual energy X-ray absorptiometry (DXA) measures, (2) compare inverse dynamic calculations of kinetics determined by DXA-calculated BSIPs (full DXA-driven inverse dynamics) against kinetics using the standard inverse dynamics approach with scaled BSIPs (scaled inverse dynamics), and (3) examine associations between full DXA-driven kinetics and overweight indices: body mass index (BMI) and segment mass index (SMI). Eighteen participants (10-11 years old) threw 10 fastballs that were recorded for motion analysis. DXA scans were used to calculate participant-specific BSIPs (mass, center of mass, radii of gyration) for each pitching arm segment (upper arm, forearm, hand), BMI, and SMI. The hypotheses were addressed with t-tests and linear regression analyses. The major results were that (1) DXA-calculated BSIPs differed from scaled BSIPs for each pitching arm segment; (2) calculations for shoulder, but not elbow, kinetics differed between the full DXA-driven and scaled inverse dynamics analyses; and (3) full DXA-driven inverse dynamics calculations for shoulder kinetics were more often associated with SMI than BMI. Results suggest that using participant-specific BSIPs and pitching arm, SMIs may improve evidence-based injury prevention guidelines for youth pitchers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".