Leukemia-Related Mortality in Inner Mongolia, 2008–2012
Bibliographic record
Abstract
In this study, we aimed to determine the leukemia-related mortality rates and associated sociodemographic characteristics in the Inner Mongolia region of China. We obtained data for the period 2008-2012 from the Death Registry System maintained by the Inner Mongolia Centers for Disease Control and Prevention. We computed the percentages of leukemia-related deaths and controls diagnosed by various methods and at different levels of hospitals. The χ(2) test was used to examine differences in leukemia-related mortality according to sex. We also calculated potential years of life lost (PYLL) and average years of life lost. Unconditional logistic regression models were used to analyze the effect of sociodemographic characteristics. The sex-adjusted leukemia-related mortality rate was 3.74/100 000. The mortality rate in men (4.27/100 000) was significantly higher than that in women (3.17/100 000), as was the respective PYLL (8040.5 vs. 6000.5 person-years). Mortality increased with increasing age in both men and women. The highest mortality rate was observed in those over 70 years of age for both men (18.36/100 000) and women (7.68/100 000). Men with a higher education level showed an increased risk of leukemia (odds ratio [OR] = 1.45, 95% confidence interval [CI] = 1.02-2.07, P = 0.04). In men, unemployment was associated with leukemia-related death (OR = 0.63, 95% CI = 0.42-0.95, P = 0.03). The leukemia-related mortality rate in Inner Mongolia was higher than that worldwide and that in China. A higher level of education and unemployment were associated with leukemia-related mortality in Inner Mongolia.
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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) | 0.001 | 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".