Calculating the Rate of Senescence From Mortality Data: An Analysis of Data From the ERA-EDTA Registry
Bibliographic record
Abstract
The rate of senescence can be inferred from the acceleration by which mortality rates increase over age. Such a senescence rate is generally estimated from parameters of a mathematical model fitted to these mortality rates. However, such models have limitations and underlying assumptions. Notably, they do not fit mortality rates at young and old ages. Therefore, we developed a method to calculate senescence rates from the acceleration of mortality directly without modeling the mortality rates. We applied the different methods to age group-specific mortality data from the European Renal Association-European Dialysis and Transplant Association Registry, including patients with end-stage renal disease on dialysis, who are known to suffer from increased senescence rates (n = 302,455), and patients with a functioning kidney transplant (n = 74,490). From age 20 to 70, senescence rates were comparable when calculated with or without a model. However, when using non-modeled mortality rates, senescence rates were yielded at young and old ages that remained concealed when using modeled mortality rates. At young ages senescence rates were negative, while senescence rates declined at old ages. In conclusion, the rate of senescence can be calculated directly from non-modeled mortality rates, overcoming the disadvantages of an indirect estimation based on modeled mortality rates.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 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) | 0.000 | 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 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".