Socioeconomic differentials in cause-specific mortality among South Korean adolescents
Bibliographic record
Abstract
BACKGROUND: There is inconsistent evidence regarding the presence of a socioeconomic differential in adolescent all-cause and cause-specific mortality. This study examines possible socioeconomic mortality differentials in Korean adolescents. Method A total of 330 321 boys and 311 830 girls aged 10-19, who are health insurance beneficiaries for civil servants and private school teachers of Korean Health Insurance Cooperation, were followed for 9 years (1995-2003). Parental income information was linked to national death certificate data. RESULTS: For boys, all-cause mortality showed a graded inverse relationship with income level in both 10-14 year olds (RR = 1.64, 95% CI: 1.40-1.91) and 15-19 year olds (RR = 1.68, 95% CI: 1.40-1.91). The major contributor was mortality differentials from external causes, with differentials of transport accident death the most important. Mortality from circulatory disease was higher in the lowest income groups in 15-19 year olds (RR = 2.21, 95% CI: 1.09-4.50). A significant socioeconomic gradient of non-external cause mortality was found in 15-19 year olds. For girls, socioeconomic differentials were less evident than boys. The all-cause mortality gradient for girls was smaller than for boys and only significant between the lowest and the highest tertile in both 10-14 year olds and 15-19 year olds (RR = 1.33, 95% CI: 1.02-1.72, RR = 1.38, 95% CI: 1.11-1.72, respectively). There were significant socioeconomic mortality differentials in all external causes and transport accidents and a marginally significant difference in suicide mortality for 10-19 year olds. Mortality from non-external causes showed no social gradient in girls. CONCLUSIONS: Socioeconomic differentials in all-cause mortality were observed in adolescents, even in early youth. This pattern might also apply to mortality from non-external causes, especially cardiovascular disease in 15-19 year old males.
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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 | 0.001 |
| 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".