Predicting Shoulder Force to Prevent Injury: Body Fat Mass Increases Shoulder Stress among Pitchers
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the relationship between whole body and throwing arm composition and length with throwing shoulder distraction and anterior force. Literature shows a relationship between shoulder force and pain; therefore, as well as between body mass and pain, it was hypothesized that pitchers with greater body fat mass would display increased throwing shoulder force during the acceleration phase of the softball pitch. METHODS: Thirty-eight high school softball pitchers from southern United States agreed to participate (1.69 ± 0.07 m, 74.71 ± 16.36 kg, 15.2 ± 1.1 yr). All procedures were completed within one visit. Pitchers underwent a dual-energy x-ray absorptiometry scan then threw fastballs at regulation distance while motion was measured using an electromagnetic motion capture system. Data from the three fastest pitches were averaged and analyzed. Peak shoulder distraction and anterior force during the acceleration phase of the softball pitch were extracted and used as dependent variables in two separate regression analyses. Whole body and throwing arm fat mass and lean mass, as well as throwing arm length were entered as independent variables into both regression equations. RESULTS: The first regression equation was statistically significant (F(5,37) = 13.28, P < 0.001) and explained approximately 62% of the variance in peak shoulder distraction force (R2 = 0.675, adjusted R2 = 0.624). Increased whole body fat mass (B = 13.76, SE = 5.39, β = 0.91, t = 2.553, P = 0.016) significantly predicted peak throwing shoulder distraction force. The second regression equation predicting peak anterior force was not significant. CONCLUSIONS: Increased fat mass may exacerbate the high force observed at the throwing shoulder and further increase injury susceptibility to 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".