Epidemiology of child injuries in Uganda: challenges for health policy
Bibliographic record
Abstract
Globally, 90% of road crash deaths occur in the developing world. Children in Africa bear the major part of this burden, with the highest unintentional injury rates in the world. Our study aims to better understand injury patterns among children living in Kampala, Uganda and provide evidence that injuries are significant in child health. Trauma registry records of injured children seen at Mulago Hospital in Kampala were analysed. Data were collected when patients were seen initially and included patient condition, demographics, clinical variables, cause, severity, as measured by the Kampala trauma score, and location of injury. Outcomes were captured on discharge from the casualty department and at two weeks for admitted patients. From August 2004 to August 2005, 872 injury visits for children <18 years old were recorded. The mean age was 11 years (95% CI 10.9-11.6); 68% (95% CI 65-72%) were males; 64% were treated in casualty and discharged; 35% were admitted. The most common causes were traffic crashes (34%), falls (18%) and violence (15%). Most children (87%) were mildly injured; 1% severely injured. By two weeks, 6% of the patients admitted for injuries had died and, of these morbidities, 16% had severe injuries, 63% had moderate injuries and 21% had mild injuries. We concluded that, in Kampala, children bear a large burden of injury from preventable causes. Deaths in low severity patients highlight the need for improvements in facility based care. Further studies are necessary to capture overall child injury mortality and to measure chronic morbidity owing to sequelae of injuries.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".