Effect of a rule change on concussions and other injuries in professional baseball
Bibliographic record
Abstract
Objective To evaluate the hypothesis that a rule change can lower the incidence of concussions and other injuries in professional baseball. Design Retrospective review of professional baseball’s electronic medical record system. All Minor (MiLB) and Major League Baseball (MLB) teams are required to use this system. Setting Five full MiLB and MLB seasons in the United States and Canada. Participants All players in MiLB and MLB were included. The 30 MLB clubs have 750 active players and play 162 games per season. MiLB has approximately 7500 active players on 200 teams that play 56-144 games annually. Intervention Before the 2014 season, MLB instituted a rule limiting home plate collisions between base runners and catchers. Outcome measures All concussions and other injuries at home plate from 2011 to 2015 were analysed by mechanism and player position. Main results From 2011-2013, an annual average of 117 injuries occurred at home plate in both MiLB and MLB, with an average of 4823 days lost annually. An average of 13 concussions occurred annually in both MiLB and MLB. Following the rule change, in 2014-2015 there was an annual average of 60 home plate injuries with 1592 days lost. There were 6 MiLB home plate concussions in 2014 and none in 2015. There were no home plate concussions in MLB in 2014-2015 (p=0.07). MiLB and MLB combined for approximately 660,000 athlete exposures per season. Conclusions This rule change was associated with a strong trend toward reduced concussions and other injuries at home plate. Competing interests Mr. D’Angelo and Mr. Coyles are employed by Major League Baseball. Dr. Green receives payment from Major League Baseball for his role as Medical Director. Dr. Valadka is a paid consultant to Major League Baseball. The authors have no other disclosures to report.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".