Risk Factors for the Development of Shoulder Pain in Elite Sled Hockey Players
Bibliographic record
Abstract
BACKGROUND: Shoulder pain is one of the most common injuries in adaptive athletes. There are minimal prior studies that investigate shoulder pain prevalence and associated risk factors in sled hockey players. OBJECTIVE: To characterize the prevalence of shoulder pain in elite-level adaptive sled hockey athletes and identify associated risk factors. DESIGN: Cross-sectional observational study. SETTING: 2019 USA Sled Hockey Classic in Chicago, IL from 7 February 2019 to 10 February 2019. PARTICIPANTS: Eighty-two elite sled hockey athletes who participated in a nationally sanctioned sports event. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: The primary outcome of the study was to describe the experience of shoulder pain using player-reported outcomes of pain including: binary (yes/no) pain reporting in the last month, Performance-Corrected Wheelchair User's Shoulder Pain Index (PC-WUSPI) reporting pain in the last week, and Visual Analog Scale (VAS) reporting pain in the last month. Associations were assessed between the measurements of pain and characteristics of participants. RESULTS: Of all participants, 70.5% endorsed shoulder pain in the last month. The average VAS for the past month was 2.13 and average PC-WUSPI for the past week was 15.46. Statistically significant associations were found between endorsement of pain in the last month and specific correlative factors including increased weight (P value .008; odds ratio [OR] 1.04, 95% confidence interval [CI] 1.01-1.07) and increased duration of manual wheelchair use (P-value .002; OR 1.13, 95% CI 1.04-1.22). CONCLUSION: Elite-level sled hockey athletes commonly report experiencing shoulder pain. There is evidence that an elite-level sled hockey player's weight and longer duration of manual wheelchair use are both associated with a greater likelihood for self-reporting shoulder pain rather than number of years of playing the sport.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".