Unintentional injury deaths among children younger than 5 years of age in India: a nationally representative study
Bibliographic record
Abstract
OBJECTIVE: To determine the mortality burden associated with unintentional injuries among children younger than 5 years of age in India. METHODS: The Registrar General of India conducted verbal autopsy for all deaths occurring in 2001-2003 in a nationally representative sample of over 1.1 million homes. These verbal autopsy reports were coded by two of 130 trained physicians, who independently assigned an ICD-10 code to each death. Discrepancies were resolved through reconciliation and, if necessary, adjudication. The probability of death during the first 5 years of life (per 100,000 live births) was estimated from the 2005 United Nations' population and death estimates for India, to which the proportions of deaths from the mortality study were applied. RESULTS: Unintentional injuries were the sixth leading cause of death among children under 5 years of age. In 2005, unintentional injuries led to 82,000 deaths (99% CI 71,000 to 88,000) among children under 5 years of age, a mortality rate per 100,000 live births (MR) of 302 (99% CI 262 to 323). Mortality was higher in rural areas (MR=339, 99% CI 282 to 351), mostly due to more drowning deaths, than in urban areas (MR=173, 99% CI 120 to 237), where falls were the leading cause of child injury mortality. CONCLUSION: Unintentional injuries, specifically drowning and falls, lead to substantial mortality in children younger than 5 years of age in India. There is a need for continued monitoring of the injury burden and investigation of risk factors for evidence-based effective injury prevention programmes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".