Cause-Specific Gender Differences in Potential Gains in Life Expectancy at Birth in Japan, 1965–2010.
Bibliographic record
Abstract
INTRODUCTION: Potential gains in life expectancy (PGLE) are an effective indicator for measuring the impacts of causes of death. This study aims to measure gender differences in PGLEs at birth by eliminating major causes of death and to discover relative important causes explaining gender gaps in life expectancy in Japan. METHODS: Data of PGLEs due to the elimination of eleven kinds of cause of death by gender, were obtained from Japanese official websites (e.g. Ministry of Health, Labor and Welfare; National Institute of Population and Social Security Research). The study covered a period of 1965–2010. The PGLEs were measured using cause elimination life table technique. Gender differences in PGLE were calculated by PGLE in Male and in Female. Figures were displayed with Excel 2013. RESULTS: Cancer, heart diseases and cerebrovascular diseases had been the dominant causes of death since 1970s for both genders. PGLE in cancer increased till 1990s and then kept stable with about four years for male and three years for female. PGLE in cerebrovascular diseases decreased over the study period from three years to one year for both genders (Figure 1). Gender differences in PGLE in cancer kept increasing with a peak value of 1.17 years in 1996 and then fluctuated slightly remaining above the level of 0.9 years. Gender differences in PGLE in suicide increased from 0.06 years in 1965 to 0.42 years in 2010. However, gender differences in PGLE in accidents had decreased from 0.98 year to 0.22 year (Figure 2 & Figure 3).
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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 | 0.001 |
| 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".