Range of Motion Adaptations During a Simulated Game Exposure in Softball Pitchers
Bibliographic record
Abstract
Hip and shoulder range of motion (ROM) alterations are correlated with increased risk of injury in softball athletes. The purpose of this study was to investigate bilateral shoulder and hip ROM adaptation during a simulated softball double-header exposure. It was hypothesized that shoulder and hip ROM would decrease after simulated game exposure and would not return to baseline following a 30-minute break. Thirty (14.8±1.9 yrs, 162.5±18.3 cm, 71.79±16.03 kg) adolescent softball pitchers participated. Testing included: bilateral hip and shoulder ROM (preSG), simulated game exposure (100 pitches), bilateral hip and shoulder ROM (postSG), 30-minute break, bilateral hip and shoulder ROM (preDH), pitching first inning of a simulated double header (12 pitches), and bilateral hip and shoulder ROM (postDH). Two separate repeated measures multivariate analyses of variance tests determined differences in ROM between time points. Dominant shoulder internal rotation ROM and non-dominant shoulder internal and external rotation ROM significantly decreased from preDG. Stride hip external rotation ROM and drive hip internal and external rotation ROM significantly decreased from preSG to preDH. Key results revealed the break given between games may not be sufficient recovery time for hip ROM. Thus, assessing ROM as workload progresses can be a useful strategy for monitoring a softball pitcher's injury risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".