Epidemiology of paediatric injuries in Rwanda using a prospective trauma registry
Bibliographic record
Abstract
BACKGROUND: Child survival initiatives historically prioritized efforts to reduce child morbidity and mortality from infectious diseases and maternal conditions. Little attention has been devoted to paediatric injuries in resource-limited settings. This study aimed to evaluate the demographics and outcomes of paediatric injury in a sub-Saharan African country in an effort to improve prevention and treatment. METHODS: A prospective trauma registry was established at the two university teaching campuses of the University of Rwanda to record systematically patient demographics, prehospital care, initial physiology and patient outcomes from May 2011 to July 2015. Univariable analysis was performed for demographic characteristics, injury mechanisms, geographical location and outcomes. Multivariable analysis was performed for mortality estimates. RESULTS: Of 11 036 patients in the registry, 3010 (27·3 per cent) were under 18 years of age. Paediatric patients were predominantly boys (69·9 per cent) and the median age was 8 years. The mortality rate was 4·8 per cent. Falls were the most common injury (45·3 per cent), followed by road traffic accidents (30·9 per cent), burns (10·7 per cent) and blunt force/assault (7·5 per cent). Patients treated in the capital city, Kigali, had a higher incidence of head injury (7·6 per cent versus 2·0 per cent in a rural town, P < 0·001; odds ratio (OR) 4·08, 95 per cent c.i. 2·61 to 6·38) and a higher overall injury-related mortality rate (adjusted OR 3·00, 1·50 to 6·01; P = 0·019). Pedestrians had higher overall injury-related mortality compared with other road users (adjusted OR 3·26, 1·37 to 7·73; P = 0·007). CONCLUSION: Paediatric injury is a significant contributor to morbidity and mortality. Delineating trauma demographics is important when planning resource utilization and capacity-building efforts to address paediatric injury in low-resource settings and identify vulnerable populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".