Pedestrian injuries in school-attending children: a comparison of injury data sources in a low-income setting
Bibliographic record
Abstract
OBJECTIVE: To estimate and compare the rate of pedestrian injuries in primary school-attending children of urban Uganda using different data sources. DESIGN: Data collection from a hospital-based trauma registry, police data, teacher reports, and a cross-sectional community-based survey. SETTING: Kawempe, the largest urban district in the capital Kampala, Uganda. Patients or SUBJECTS: Primary school-attending children aged 4-12 from 39 randomly selected schools were included in the trauma registry, police data, and teacher reports. 1828 households randomly selected from the 39 schools were interviewed for the community survey. MAIN OUTCOME MEASURE: A pedestrian injury. For the trauma registry-defined as a pedestrian injury resulting in a visit to the hospital. For the police data-defined as a pedestrian injury reported to the police. For the teacher reports and survey-defined as a pedestrian injury resulting in at least a day off school. RESULTS: The estimated pedestrian injury rates per 100 000 person-years were 54.0 (95% CI 25.3 to 117.4), <53.97 (95% CI 23.8 to 125.9), 1878.8 (95% CI 1513.1 to 2322.4), and 764.0 (95% CI 523.3 to 1117.2) from the trauma registry, police data, teacher reports, and community survey, respectively. CONCLUSIONS: Pedestrian injury rates differed significantly between different data sources. Users must be aware of the different target populations, definitions, and limitations of the data sources before direct comparisons are made. Injury reports by volunteer teachers may be a feasible source of injury data in other low/middle-income countries.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".