Understanding the Burden of Pediatric Traumatic Injury in Uganda: A Multicenter, Prospective Study
Bibliographic record
Abstract
INTRODUCTION: Traumatic injury is responsible for eight million childhood deaths annually. In Uganda, there is a paucity of comprehensive data describing the burden of pediatric trauma, which is essential for resource allocation and surgical workforce planning. This study aimed to ascertain the burden of non-adolescent pediatric trauma across four Ugandan hospitals. METHODS: We performed a descriptive review of four independent and prospective pediatric surgical databases in Uganda: Mulago National Referral Hospital (2012-2019), Mbarara Regional Referral Hospital (2015-2019), Soroti Regional Referral Hospital (SRRH) (2016-2019), and St Mary's Hospital Lacor (SMHL) (2016-2019). We sub-selected all clinical encounters that involved trauma. The primary outcome was the distribution of injury mechanisms. Secondary outcomes included operative intervention and clinical outcomes. RESULTS: There was a total of 693 pediatric trauma patients, across four hospital sites: Mulago National Referral Hospital (n = 245), Mbarara Regional Referral Hospital (n = 29), SRRH (n = 292), and SMHL (n = 127). The majority of patients were male (63%), with a median age of 5 [interquartile range = 2, 8]. Chiefly, patients suffered blunt injury mechanisms, including falls (16.2%) and road traffic crashes (14.7%) resulting in abdominal trauma (29.4%) and contusions (11.8%). At SRRH and SMHL, from which orthopedic data were available, 27% of patients suffered long-bone fractures. Overall, 55% of patients underwent surgery and 95% recovered to discharge. CONCLUSIONS: In Uganda, non-adolescent pediatric trauma patients most commonly suffer injuries due to falls and road traffic crashes, resulting in high rates of abdominal trauma. Amid surgical workforce deficits and resource-variability, these data support interventions aimed at training adult general surgeons to provide emergency pediatric surgical care and procedures.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".