Epidemiology of injuries presenting to the national hospital in Kampala, Uganda: implications for research and policy
Bibliographic record
Abstract
BACKGROUND: Despite the growing burden of injuries in LMICs, there are still limited primary epidemiologic data to guide health policy and health system development. Understanding the epidemiology of injury in developing countries can help identify risk factors for injury and target interventions for prevention and treatment to decrease disability and mortality. AIM: To estimate the epidemiology of the injury seen in patients presenting to the government hospital in Kampala, the capital city of Uganda. METHODS: A secondary analysis of a prospectively collected database collected by the Injury Control Centre-Uganda at the Mulago National Referral Hospital, Kampala, Uganda, 2004-2005. RESULTS: From 1 August 2004 to 12 August 2005, a total of 3,750 injury-related visits were recorded; a final sample of 3,481 records were analyzed. The majority of patients (62%) were treated in the casualty department and then discharged; 38% were admitted. Road traffic injuries (RTIs) were the most common causes of injury for all age groups in this sample, except for those under 5 years old, and accounted for 49% of total injuries. RTIs were also the most common cause of mortality in trauma patients. Within traffic injuries, more passengers (44%) and pedestrians (30%) were injured than drivers (27%). Other causes of trauma included blunt/penetrating injuries (25% of injuries) and falls (10%). Less than 5% of all patients arriving to the emergency department for injuries arrived by ambulance. CONCLUSIONS: Road traffic injuries are by far the largest cause of both morbidity and mortality in Kampala. They are the most common cause of injury for all ages, except those younger than 5, and school-aged children comprise a large proportion of victims from these incidents. The integration of injury control programs with ongoing health initiatives is an urgent priority for health and development.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".