Statistical methods for estimating the environmental burden of disease in Canada, with applications to mortality from fine particulate matter
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we describe statistical approaches for estimating the burden of disease. Specifically, we present life‐table methods that can be used to determine years of life lost ( YLL ) due to the disease of interest. In addition, we propose a new variance estimator for the life table based estimator of YLL and demonstrate its accuracy through computer simulation. We also indicate how the population attributable fraction ( PAF ) of the disease can be calculated in relation to a known risk factor for the disease. The PAF effectively represents the fraction of the disease burden that would be eliminated in the absence of the risk factor of interest. Finally, we illustrate the use of these methods in assessing the PAF for all cause, lung cancer and cardiopulmonary mortality associated with ambient concentrations of fine particulate matter present in ambient air in Canada. Copyright © 2012 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it