A comparative study of mortality data for all causes and unintentional injuries between Japan and other developed countries
Bibliographic record
Abstract
Background It is true that Japan is one of the lowest infant mortality countries in the world. However unintentional injury is the leading cause of death, as in other developed countries. This trend has been true for more than 30 years, so unintentional injury death has received much attention in Japan recently. Purpose The purpose of this study is to examine Japanese mortality data for all causes and unintentional injury among developed countries. Methods Compare Japanese mortality data for all causes and unintentional injuries with 13 developed countries, Australia, Austria, Belgium, Canada, France, Germany, Italy, Netherlands, Spain, Sweden, Switzerland, UK and USA. These mortality data are available from World Health Statistics Annual. Results As a consequence of comparison, mortality rate for all causes of death among under 1-year-old, 5–14 year olds, 55–64-year-olds, 65–74-year-olds and over 75-year-olds in Japan are almost lowest in these developed countries. However, unintentional injury death rates for these age groups are not so low. Also, mortality rate for all causes of 1–4-year-olds and unintentional Injury death of under 1-year-old, 55–64-year-olds and 65–74-year-olds in Japan are higher than the average mortality rate of 13 advanced countries. Significance These maybe caused by Japanese insufficient emergency care system and structure, and lack of injury prevention materials. Also, it is necessary for Japanese to spread the knowledge of first aid for injury.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".