Incidence of Elbow Dislocations in the United States Population
Bibliographic record
Abstract
BACKGROUND: There is minimal published information regarding the epidemiology of simple elbow dislocations. The purpose of this study was to report the estimated incidence of elbow dislocations in the United States, with use of the National Electronic Injury Surveillance System (NEISS) database. METHODS: The NEISS database includes 102 hospitals representing a random sampling of all patients presenting to U.S. emergency departments. The database was queried for elbow dislocation events. NEISS data for 2002 through 2006 were used for raw data and weighted injury counts. Incidence rates with 95% confidence intervals (95% CI) were calculated by age group and sex, with use of U.S. census data. RESULTS: One thousand and sixty-six elbow dislocations were identified, representing a weighted estimate of 36,751 acute dislocations nationwide. A calculated incidence of 5.21 dislocations per 100,000 person-years (95% CI, 4.74 to 5.68) was noted. The highest incidence of elbow dislocations (43.5%) occurred in those who were ten to nineteen years old (6.87 per 100,000 person-years; 95% CI, 5.97 to 7.76). The incidence rate ratio for the comparison of dislocations in males with those in females was 1.02 (5.26 per 100,000 for males and 5.16 per 100,000 for females). In patients ten years or older, 474 injuries (44.5% of total dislocations) were sustained in sports. Males dislocated elbows in football, wrestling, and basketball. Females sustained elbow dislocations most frequently in gymnastics and skating activities. CONCLUSIONS: The estimated incidence of elbow dislocations in the U.S. population is 5.21 per 100,000 person-years, with use of a national database. Adolescent males are at highest risk for dislocation. Nearly half of acute elbow dislocations occurred in sports, with males at highest risk with football, and females at risk with gymnastics and skating activities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".