Single-Leg Squat and Reported Pain in Collegiate Softball Pitchers
Bibliographic record
Abstract
Background: Single-leg squat (SLS) performance is related to altered mechanics related to injury during the windmill softball pitch; however, it is unknown if SLS kinematics differ between softball pitchers with and without upper extremity pain. Purpose/Hypothesis: The purpose of this study was to compare knee valgus, trunk rotation, trunk lateral flexion, and trunk flexion during an SLS in collegiate softball pitchers with and without self-reported upper extremity pain. It was hypothesized that those who reported upper extremity pain would show increased compensatory trunk and knee kinematics compared with those without pain. Study Design: Controlled laboratory study. Methods: A total of 75 collegiate softball players (mean age, 20.4 ± 1.7 years; mean height, 173.3 ± 7.7 cm; mean weight, 79.1 ± 11.6 kg) participated and were placed in pain (n = 20) or no-pain (n = 55) groups. Participants performed an SLS once per side. Kinematic data were collected at 100 Hz using an electromagnetic tracking system. A 2 (pain vs no pain) × 2 (descent vs ascent) × 2 (drive leg vs stride leg) mixed-design multivariate analysis of variance with Wilks lambda distribution was used to determine differences in drive-leg and stride-leg lower body mechanics between the descent and ascent phases of the SLS between the pitchers in the current study with and without pain. Results: = 0.717). Conclusion: Study findings indicated that SLS mechanics do not differ between collegiate softball pitchers with and without reported upper extremity pain. Drive-leg mechanics showed more stability in the SLS than stride-leg mechanics. Clinical Relevance: Softball pitchers are at risk of upper extremity injury. It is important to identify mechanisms that may lead to pain in order to mitigate the risk of injury.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".