Smartphone-Related Accidents in Children and Adolescents
Bibliographic record
Abstract
OBJECTIVES: Smartphones have become an integral part of daily life, often grabbing full attention of its user. We hypothesized that smartphone-associated trauma in children and adolescents has increased in the last decade. The objective of this study was to analyze smartphone-related injuries in children at two German centers for pediatric emergency care. METHODS: Smartphone-related injuries were recorded between January 2008 and March 2018 at two centers of pediatric surgery in Germany. Data were assessed for patient demography, cause of accident, type of injury, treatment, and outcome. RESULTS: Ten children (8 girls, 2 boys; mean ± SD age, 10.6 ± 6.0 years; range, 10 weeks to 17 years) were included. Two patients were injured in 2008 to 2015, eight in 2016 to 2018, of which three required hospital admissions. Six accidents happened in public spaces, and four within domestic environments. Eight children (mean ± SD age, 13.3 ± 2.4 years; 7 girls) were injured while using their smartphone, therefore being distracted. Two children (mean ± SD age, 6.5 ± 6.4 months) were involuntarily hurt by the smartphone of their caregivers. The causes of accident and related injuries were highly variable and ranged from minor trauma (mild head injury [n = 3], abrasions [n = 2], bruises of fingers [n = 2]/hand [n = 1]/ankle [n = 2]) to major injuries requiring intensive care treatment (pelvic [n = 1] or vertebral body fractures [n = 1]). CONCLUSIONS: Smartphone-associated injuries mainly caused by distraction gain increasing importance in pediatric traumatology. The frequency is higher in females compared with their male counterparts. The prevention of these accidents should become part of educational programs for children and adolescents.
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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) | 0.001 | 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 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".