Childhood injury surveillance in a Nigerian teaching hospital
Bibliographic record
Abstract
Background Information on childhood injury in Nigeria is scanty. There is a need to establish and strengthen childhood injury monitoring and reporting as a useful tool for childhood injury control. Aims/Objectives/Purpose The aim of this pilot study was to develop a childhood injury surveillance system at University of Ilorin Teaching Hospital, Nigeria. Methods All children aged ≤15 years presenting with injuries at the emergency room (ER) during a 10-month period were included. A structured form was designed to obtain information on injury history from parents/guardians. Clinical details were extracted from medical records. Data were compared using χ2/Fisher's exact test as appropriate. Results/Outcome A total of 111 children presented to the ER with injuries (10% of all patients seen). The median age was 72 months, with a male : female ratio of 1.9 : 1. Leading mechanisms of unintentional injuries were road traffic crashes (RTC) (44%), falls (37%) and burns (13%). In 58% of RTCs, the injured child was a pedestrian. Injuries mostly occurred in homes (43%) and on streets/highways (39%). Major injuries involved the head and neck (42%) and extremities (27%). Most injuries were moderate to severe (76%) with 12% resulting in death. RTCs accounted for 46% mortality. Mortality was significantly influenced by age (p=0.028), nature of injury (p=0.013) and injury severity (p=0.000). Significance /Contribution to the Field The injury surveillance system provided valuable information on childhood injuries presenting to the ER. The results suggest that improved home and road safety and increased parental supervision are needed to reduce childhood injury.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".