Evaluation of Hip Characteristics in Baseball and Softball Athletes with and Without Throwing Arm Pain
Bibliographic record
Abstract
During throwing, the lower extremity assists in the generation and transfer of momentum. Lower extremity stability assists in this transfer by providing a base for distal mobility of the arm segments. This study aimed to determine differences between hip rotational range of motion and strength based on the presence of throwing-arm pain (yes/no) and throwing sport (baseball/softball). We hypothesized those experiencing pain would display decreases in hip range of motion and strength, and that softball players would display greater range of motion than baseball, but less strength based on sex-specific characteristics. Forty-four baseball (13±2 years, 165.2±13.0 cm, 58.5±13.4 kg) and 50 softball players (13±2 years, 160.9±11.2 cm, 62.7±17.9 kg) participated. Multivariate analysis of variance tests (2×2) determined differences in bilateral hip range of motion, total arc of motion, and strength between pain status and sport. There were no significant interactions (>0.05) for pain status and sport on hip range of motion, total arc of motion, and strength. Furthermore, no significant main effects (>0.05) were found for pain status or sport alone on range of motion or strength. Future work should be directed at explaining the effects of hip characteristics on the throwing motion and how it equates to throwing-arm health.
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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 | 0.001 | 0.001 |
| 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".