Life expectancy with chronic kidney disease: an educational review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Can renal prognosis and life expectancy be accurately predicted? Increasingly, the answer is yes. The natural history of different forms of renal disease is becoming clearer; the degree of reduction in glomerular filtration rate (GFR) and the magnitude of proteinuria are strong predictors of renal outcome. Actuarial data on life expectancy from the start of renal replacement therapy are available from renal registries such as the U.S. Renal Data System (USRDS), and the UK Renal Registry. Recently, similar data have become available for patients with chronic kidney disease. Data collected from a large population-based registry in Alberta, Canada and stratified for different levels of estimated GFR (eGFR) have shown that the reduction in life expectancy with kidney failure is not a uremic event associated with starting dialysis but a continuous process that is evident from an eGFR of ≤60 ml/min. Nevertheless, despite the poor prognosis of the last stages of renal failure, progress in the treatment and management of these patients and, in particular, of their cardiovascular risk factors continues to improve long-term outcome.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.007 | 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