Long-term risk projection and its application to nephrology research
Bibliographic record
Abstract
Measures of kidney disease burden or risk estimates in nephrology research have primarily focused on the concepts of prevalence, annual incidence or relatively short-term risk such as five or ten-year risk. The concept of long-term risk is rarely used in nephrology research. This paper focuses on two long-term risk measures-lifetime risk and life expectancy. Lifetime risk is an epidemiologic measure that expresses the probability that a person who is currently free of the condition will acquire it at some time during the remainder of their expected lifespan. Life expectancy is the expected number of years of life remaining for a given group of individuals at a specified age. Key data required for estimation of lifetime risk and life expectancy are disease incidence and mortality derived by considering age in the time scale in a longitudinal study. Lifetime risks can be estimated from incidence and mortality rates derived from prospective studies whereas mortality rates are required to estimate life expectancy. Although short-term risks are important, long-term risk can be particularly beneficial for future prediction of the burden of kidney disease, and to assist in health planning and public education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".