Characteristics of severe injuries among children and adolescents in a sub-national trauma registry in Saudi Arabia
Bibliographic record
Abstract
Introduction: Children and adolescents constitute nearly 30% of the global population, and injuries within this age group represent a significant public health concern worldwide. Methods: A cross-sectional study was conducted across five hospitals within the MNG-HA. The Trauma Registry database extracted all pediatric patients with injuries from 2015 to 2022 who were admitted for at least 1 day to MNG-HA hospitals. The outcome was severe injuries, defined as an Injury Severity Score (ISS) ≥16, and the characteristics of the patients included various demographic, health, and injury-related factors. Bivariate and multivariate logistic regression analyses were reported separately for each age group (0-6, 7-12, and 13-19 years). Results: Out of 3,382 patients, 16.8% experienced severe injuries, with a higher prevalence among males. Significant associations with severe injuries included drowning, poisoning, and assault in the 0-6 age group; abdominal/spine injuries and motor vehicle crashes, in the 7-12 age group; and poisoning and intentional self-harm in adolescents. Conclusion: This study identifies critical factors associated with severe injuries across age groups, informing targeted interventions to reduce injury risk in Saudi Arabia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".