Shoulder and elbow injuries in professional baseball pitchers
Bibliographic record
Abstract
Baseball is a sport that is played across the world, with significant roots in the United States, Latin America, and Asia. Major League Baseball (MLB) is the premier professional baseball league in the world and has affiliates in the United States and Canada. MLB reportedly exceeded $US10 billion in revenue in 2017, with 15 consecutive years of record-breaking revenue from the years 2003 to 2017. With the significant financial stakes involved in MLB, it is imperative to field the most talented players as often as possible to attract consistent crowds and a winning culture for the team. The pitcher plays a crucial role in baseball, acting as the cornerstone of a team’s defensive capacity. A talented pitcher could significantly limit the run-scoring potential of opposing teams, greatly enhancing their own team’s chances of winning the game. However, due to their high throwing volumes, pitchers are more susceptible to arm injuries compared to other players in the team. This issue has led to ongoing debate regarding the which variables are most associated with shoulder and elbow injury risk in professional baseball pitchers. This thesis aims to address some of the key deficiencies in the scientific literature by: 1. Summarising the current literature on shoulder and elbow injuries and their associated risk modifiers in professional baseball pitchers. 2. Developing a generalized additive mixed model alongside a linear mixed effects regression model to predict humeral torsion and determine their performance error compared to a null model, along with the standard error of ultrasound when performing this assessment. 3. Developing a generalized additive mixed model using preseason shoulder rotational range of motion measures and various demographic data as predictor variables to predict injury in the upcoming season.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".