Epidemiology of unintentional child injuries in the South-East Asia Region: a systematic review
Bibliographic record
Abstract
All the 11 members of the South-East Asia Region (SEAR) of the World Health Organization are categorised as low- and middle-income countries. This region has over a quarter of the world's total population but comprises about one-third of the world's unintentional injury-related deaths. There is a paucity of good-quality mortality and morbidity data from most of these countries. This is the first systematic review of community-based surveys on child injuries that summarises evidence from child injury studies from the SEAR countries. The included papers reported varying estimates of overall non-fatal unintentional injury rates across the countries, from 15/1000 children in Thailand to as high as 342/1000 children in India. The fatal injury rates were also found to be varying. This review revealed a need for strengthening child injury research using standard methodologies across the region and for promoting the dissemination of the results.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".