Incidence, patterns and risk factors for injuries among Ugandan children
Bibliographic record
Abstract
There is limited epidemiological data on childhood injuries in developing countries. This study assessed the incidence, patterns and risk factors for injuries among children aged 0-5 years in Wakiso District, Uganda. To determine differences, chi-square and Wilcoxon rank sum tests were used. Risk factors were assessed using Poisson regression. Overall, information from 359 children of mean age 32 months (SD: 18.4) was collected. Annual incidence of injuries was 69.8 per 1000 children/year (95% CI 58.8-80.8). One fatal injury due to burns was reported. Incidence of injuries was less associated with being female (IRR: 0.56, 95% CI 0.34-0.90) and increasing age of the caretaker (IRR: 0.96, 95% CI 0.92-0.99). The high incidence of childhood injuries necessitates the need for interventions to reduce injuries among children.
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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 | 0.000 | 0.003 |
| 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 | 0.000 | 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".