A Baseball Injury and Performance Initiative to Combat Health Risks Associated With Early Sport Specialization
Bibliographic record
Abstract
ABSTRACT Ulnar collateral ligament (UCL) injuries have been increasing steadily for the past decade, especially among youth and adolescent amateurs. USA Baseball's Pitch Smart guidelines have been introduced to combat UCL and other throwing arm injuries because overuse is the paramount cause. Fatigue is also a major contributor to injuries. Other factors include inadequate strength, recovery methods, and parental and caregiver education. Significant misconceptions exist among parents and caregivers about UCL injury, surgical repair, strength and conditioning, and injury prevention that can directly influence their children's health and safety in sport. Therefore, parents' and caregivers' education is imperative to reduce injuries in youth baseball. This article presents an educational opportunity by providing an evidence-based training program designed to prevent injuries and maximize performance called the Baseball Injury and Performance Initiative 10 (BIPI 10). The BIPI 10 program conditions baseball players through whole-bodied movement, varying contraction tempos, and plyometrics that are sport specific. BIPI 10 is believed to offer high compliance because training can be completed daily in less than 10 minutes and initiated anywhere across competitive levels on a worldwide scale in a similar fashion to the Fédération Internationale de Football Association (FIFA) program FIFA 11+ for soccer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".