Health-adjusted potential years of life lost due to treatable causes of death and illness.
Bibliographic record
Abstract
BACKGROUND: Summary measures based on potential years of life lost (PYLL) to death and to illness would complement population health measures such as health-adjusted life expectancy. These measures can be applied to deaths and to conditions that are considered amenable to treatment by the health care system. DATA AND METHODS: Life tables for 2007 to 2009 were used to calculate health-adjusted potential years of life lost (HAPYLL) for males and females from birth to age 75 for Canada and the provinces. Mortality rates for all causes were adjusted using the Health Utility Index 3 (HUI3) as a measure of the average value of a year in ill health. Average HUI3 was calculated for each age group for selected health conditions self-reported in the 2009/2010 Canadian Community Health Survey. HAPYLL was estimated by adding the average number of years lost due to treatable causes of death (treatable PYLL) to the average number of years lost because of ill health (HUI3 gap). RESULTS: More years of life are lost because of ill health than are lost because of premature death. During the 2007-to-2009 period, age-/sex-standardized PYLL due to treatable causes of death was 1,257 years per 100,000 person-years, while the age-/sex-standardized HUI3 gap was 6,477 years. Provincial rankings change when information on deaths is combined with information on ill health. INTERPRETATION: The impact of treatable conditions is greater in terms of quality of life lost than in life-years lost.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".