Causes of Death in Children Aged < 15 Years in the Inner Mongolia Region of China, 2008-2012
Bibliographic record
Abstract
The objective of our study was to identify the causes of death in children <15 years of age in Inner Mongolia and to examine the age-specific causes of death. Study data from 2008-2012 were obtained from the Death Registry System that is maintained by the Inner Mongolia Centers for Disease Control and Prevention. The mortality rate (per 100,000) for children <15 years of age was calculated and stratified by age in different years. We computed the proportion of age-specific causes of death for children <15 years that occurred between 2008 and 2012 across eight monitoring points in Inner Mongolia. We used a log-linear model to analyze the year and age effects on childhood mortality. From 2008-2012, the standardized mortality of children <15 years of age was 42.78/100,000. The mortality rate was not significantly different from 2008 to 2012 (p>0.05); the mortality rate was the highest in the <1-year age group (p<0.05); and the mortality rate of the <1-year age group was higher in 2012 compared to that in 2009 (p<0.05), 2010 (p<0.05), and 2011 (p<0.05). In children aged 1-14 years, the leading cause of death was injuries, among which transport accident injuries were the most prevalent. To reduce the childhood mortality rate in Inner Mongolia, China, we should focus on the prevention of perinatal deaths in infants <1 year of age and on the prevention of transport accident injuries among older children (1-14 years).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".