Epidemiology of Shoulder Dislocations in the United States From 1990 to 2019: A Temporal Study Using the Global Burden of Disease Database
Bibliographic record
Abstract
Introduction The shoulder joint is a common site for joint dislocation, with many individuals suffering from recurrent dislocations following treatment. The purpose of this study was to evaluate the epidemiology of shoulder dislocations in the United States from 1990 to 2019. Methods The Global Burden of Disease database was utilized to collect epidemiological data on shoulder dislocations in the United States (U.S.) from 1990 to 2019. These data included age-standardized rates of years lived with disability (YLDs), prevalence rates, and incidence rates per 100,000 people. Using the U.S. Census Bureau definitions, the data were stratified into four regions: the Northeast, Midwest, South, and West. Bartlett's test was used to assess whether the variance of the dataset was equal. Welch's ANOVA was performed to assess differences in YLDs, prevalence rates, and incidence rates between regions. Results From 1990 to 2019, there was an 8.69% decrease in mean YLDs, an 8.69% decrease in prevalence rates, and a 9.14% decrease in mean incidence rates of shoulder dislocations. Women experienced a 0.78% increase in mean YLDs, a 0.77% increase in mean prevalence rates, and a 0.27% increase in mean incidence rates of shoulder dislocation. Men experienced a 15.45% decrease in mean YLDs, a 15.45% decrease in mean prevalence rates, and a 15.82% decrease in mean incidence rate of shoulder dislocations. Regardless of region, men were more likely to experience a higher mean rate of YLDs (1.06 vs. 0.79, p<0.001), higher mean prevalence rates (17.16 vs. 12.70, p<0.001), and higher mean incidence rates (115.25 vs. 84.59, p<0.001) of shoulder dislocations. The West region experienced the highest mean rate of YLDs, the highest mean prevalence rates, and the highest mean incidence rates of shoulder dislocation. The Northeast region experienced the lowest mean rates of YLDs, mean prevalence rates, and mean incidence rates. Men experienced higher mean rates of YLDs, prevalence, and incidence of shoulder dislocations compared to women (p<0.001). Conclusion From 1990 to 2019, the U.S. witnessed a decline in mean YLDs, incidence, and prevalence rates for shoulder dislocations. This trend varied by gender, with men experiencing notable decreases across these metrics, while women saw slight increases. Overall, men consistently had higher rates of shoulder dislocations compared to women. Geographically, the Western region had the highest rates, whereas the Northeast had the lowest.
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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 | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".