Estimating (quality‐adjusted) life‐year losses associated with deaths: With application to COVID‐19
Why this work is in the frame
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Bibliographic record
Abstract
Many epidemiological models of the COVID-19 pandemic have focused on preventing deaths. Questions have been raised as to the frailty of those succumbing to the COVID-19 infection. In this paper we employ standard life table methods to illustrate how the potential quality-adjusted life-year (QALY) losses associated with COVID-19 fatalities could be estimated, while adjusting for comorbidities in terms of impact on both mortality and quality of life. Contrary to some suggestions in the media, we find that even relatively elderly patients with high levels of comorbidity can still lose substantial life years and QALYs. The simplicity of the method facilitates straightforward international comparisons as the pandemic evolves. In particular, we compare five different countries and show that differences in the average QALY losses for each COVID-19 fatality is driven mainly by differing age distributions for those dying of the disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it