The Impact of Improved Population Life Expectancy in Survival Trend Analyses of Specific Diseases
Bibliographic record
Abstract
BACKGROUND: Survival trend analyses examine mortality outcomes over time. The impact of conducting survival trend analyses without accounting for improved population survival has not been systematically studied. METHODS: The 1-year risk of death in the 100 most common hospital admissions for Ontario adults in 1994, 1999, 2004, and 2009 was determined. Generalized linear models were used to determine if adjusted death risk changed significantly over time with and without accounting for population survival. RESULTS: The statistical significance of temporal trends in survival changed after accounting for population life expectancy in 16 diagnoses (16 percent) (in 13 of 55 diagnoses, statistically significant decreasing mortality trends became insignificant; in 3 of 15 diagnoses, insignificant trends changed to a significant increase in mortality risk over time). CONCLUSIONS: These results highlight the importance of accounting for population life-expectancy changes in survival trend analyses.
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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 | 0.052 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".