Shoulder Internal Rotator Strength as Risk Factor for Shoulder Pain in Volleyball Players
Bibliographic record
Abstract
The aim of this study was to identify the intrinsic factors that could increase risk of shoulder pain in adolescent volleyball players. Twenty-eight young male volleyball players (between 14 and 18 years old) participated in this study. Athletes were submitted to: isokinetic muscle strength assessment of shoulder rotator muscles, ball service speed assessment, anterior and posterior drawer test, apprehension test, groove sign and scapular dyskinesia test. Athletes were followed for 16 weeks to monitor the presence of shoulder pain. All athletes were submitted to the same training protocol. During the 16 weeks, 28.5% of the athletes (n=8) experienced shoulder pain in the dominant limb higher than 3 according to Numerical Rating Scale criteria; 71.5% of the athletes (n=20) did not experience pain, or pain equal or lower than 3. The main result of our study was that the odds of feeling pain higher than 3 was significantly higher among players who presented higher values for internal rotation peak torque (OR=1.113, CI 95%=1.006 to 1.232 and p=0.038). The odds of feeling pain increased by 11% for every N·m of the internal rotator muscles. Pre-season isokinetic rotator strength assessments can help identify adolescent volleyball players at increased risk of a shoulder injury.
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 | 0.000 | 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) | 0.002 | 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".