Prevalence of scapular dyskinesis and shoulder pain in amateur surfers from Rio Grande do Sul: A cross-sectional study
Bibliographic record
Abstract
ABSTRACT The paddling movement represents 51.4% of total surfing practice time, generating high muscle demand for the shoulder complex. Despite this, there is a gap on the literature on the prevalence of pain and scapular dyskinesis (SD) in surfers. This study sought to evaluate the prevalence of SD and shoulder pain in amateur surfers in the state of Rio Grande do Sul, Brazil. It is a cross-sectional descriptive observational study. The sample consisted of 21 men, aged between 18 and 42 years, surfing for at least two years. The outcomes evaluated were static SD, dynamic SD, shoulder pain - by the numerical pain rating scale -, pectoralis minor muscle length, and score on the Western Ontario Shoulder Instability Index. Continuous variables were expressed in mean and standard deviation. Categorical variables were expressed as percentages. Data associations were tested through chi-square test and Pearson correlation test. SD was present in 71.4% of the sample, with a higher prevalence of Type I dyskinesia (57.1%), and 42.9% presented shoulder pain during evaluation. SD was observed in most of the studied population, while pain was present in just under half of the participants. Although SD is a very prevalent find in amateur surfers, no correlation was observed between pain and reduced life quality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".