A Model for Causality of Pitching-related Overuse Injuries in Women’s Fastpitch Softball
Bibliographic record
Abstract
Fastpitch softball is a popular women's sport, and athletes, particularly pitchers, are at high risk for overuse injury. Softball-related injury rates are low; however, the prevalence of overuse injuries is high. Injuries at the high school and collegiate levels occur early in the season, and approximately 50% of shoulder and elbow injuries in softball are attributable to overuse. Survey research showed 77% of high school pitchers pitch with pain, and 73% of collegiate pitchers reported an overuse injury in the previous season. Modifiable and non-modifiable intrinsic and extrinsic risk factors contribute to injury risk in a model that can be used in history taking, clinical examination, and management of softball-related injuries. In this manuscript, we present a scoping review of fastpitch softball injury research by competitive levels from 1990 to present. We also introduce a model for overuse injury causality in this athlete population. With this information, clinicians will be able to identify risk factors related to injury in softball pitchers. More research is needed to make evidence-based recommendations for injury prevention in this athlete population.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".