Analysis of Musculoskeletal Injuries Sustained in Falls From the United States–Mexico Border Fence
Bibliographic record
Abstract
Injuries sustained by unauthorized individuals who jump or fall from the United States-Mexico border fence are frequently treated by trauma centers in border states. The authors investigated patterns of musculoskeletal injury occurring in these individuals to improve emergency department assessment and to identify strategies to prevent future injuries. A retrospective chart review was performed for patients presenting to an urban, level I trauma center with musculoskeletal injuries sustained in a jump or fall from the United States-Mexico border fence between February 2004 and February 2010. Frequency of fracture by site, frequency of open fracture, and associated patterns of injury were recorded. The population was stratified by age and sex to identify disparity in injury pattern. Average length of stay and number of surgical interventions were also recorded. During the study period, 174 individuals who had jumped or fallen from the United States-Mexico border fence were identified. The population contained 93 (53%) women and 81 (47%) men with an average age of 31.5 years (range, 11-56 years). On average (±standard error), men sustained slightly more fractures than women (1.77±0.12 vs 1.43±0.07; P=.015). There were no significant differences in the number of fractures sustained between age groups. Average length of stay for patients admitted to the hospital was 3.5 days. Patients underwent an average of 0.75 surgical interventions during admission. Falls from the United States-Mexico border fence are a significant cause of morbidity among unauthorized immigrants. [Orthopedics. 2017; 40(3):e432-e435.].
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".