Forearm Flexor Injuries Among Major League Baseball Players: Epidemiology, Performance, and Associated Injuries
Bibliographic record
Abstract
BACKGROUND: Despite evidence highlighting the importance of the forearm flexor muscles of elite baseball players, no studies have reported on the epidemiology of flexor strains and their associated outcomes. PURPOSE: To examine the incidence, associated injuries, and outcomes associated with forearm flexor injuries among major and minor league baseball players. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Injury data attributed to forearm flexor injuries among Major League Baseball (MLB) and minor league teams between 2010 and 2014 were obtained from the professional baseball Health and Injury Tracking System. This analysis included the number of players injured, seasonal timing of injury, days spent on the disabled list (DL), preinjury performance data, and subsequent injuries. RESULTS: A total of 134 and 629 forearm flexor injuries occurred in MLB and the minor leagues, respectively. The mean player age was 28.6 and 22.8 years in the MLB and minor leagues, respectively. The mean time spent on the DL for MLB players was 117.0 days, as opposed to 93.9 days in the minor leagues ( P = .272). Interestingly, pitcher performance declined in all categories examined leading up to the season of injury, with significant differences in walks plus hits per inning pitched ( P = .04) and strike percentage ( P = .036). Of MLB players with a forearm injury, subsequent injuries included 50 (37.3%) shoulder, 48 (35.8%) elbow, and 24 (17.9%) forearm injuries. Among injured minor league players, subsequent injuries included 170 (27.0%) shoulder, 156 (24.8%) elbow, and 83 (13.2%) forearm injuries. These rates of subsequent injuries were significantly higher compared with the rates of injuries sustained among players without forearm injuries in both leagues ( P < .001). Finally, 26 (19.4%) MLB and 56 (8.9%) minor league players required an ulnar collateral ligament reconstruction, rates that were significantly higher compared with players without a flexor strain ( P < .001). CONCLUSION: Flexor-pronator injuries are responsible for considerable time spent on the DL for elite players in MLB and the minor leagues. The most significant findings of this investigation illustrate that a flexor strain may be a significant risk factor for subsequent upper extremity injuries, including an ulnar collateral ligament tear.
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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 | 0.002 |
| 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 | 0.001 |
| 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".