Trends of Alcohol Attributable Mortality in Korea: 1995-2000
Bibliographic record
Abstract
Although alcohol misuse contributes substantially to mortality from diseases, injuries and adverse effects, a few attempts have been made to figure out size of adverse consequences attributable to alcohol in Korea. This study was conducted to describe trends of estimated deaths attributable to alcohol in Korea. Estimations were made by employing Korean alcohol aetiological fraction(AEF) into deaths from alcohol-related diseases, injuries, and adverse effects from year of 1995 through 2000. Korean AEF was derived from previous studies on AEF applied to USA and Canada (Schultz et al.,1991; English et al., 1995) with reflecting peculiar drinking patterns in Korea. An average number of deaths attributable to alcohol was 21,123, accounting for 8.76% of all deaths reported to National Statistical Office during the period. Death rates attributable to alcohol tended to decrease from year of 1995 to 1997 and then increased with peak at year of 1999. Sex-age standardized alcohol attributable death rates varied among areas, with those of metropolitan areas being lower than those of non metropolitan areas. Years of potential life lost (YPLL) were estimated to reflect qualitative aspect of deaths attributable to alcohol. Similar change patterns during the year were observed between number of deaths and YPLL. Average YPLL of men was longer than that of women by about 4 years. Some implications for future study have been discussed.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".