Age Affects Outcomes in Chronic Kidney Disease
Bibliographic record
Abstract
Chronic kidney disease (CKD) is common among the elderly. However, little is known about how the clinical implications of CKD vary with age. We examined the age-specific incidence of death, treated end-stage renal disease (ESRD), and change in estimated glomerular filtration rate (eGFR) among 209,622 US veterans with CKD stages 3 to 5 followed for a mean of 3.2 years. Patients aged 75 years or older at baseline comprised 47% of the overall cohort and accounted for 28% of the 9227 cases of ESRD that occurred during follow-up. Among patients of all ages, rates of both death and ESRD were inversely related to eGFR at baseline. However, among those with comparable levels of eGFR, older patients had higher rates of death and lower rates of ESRD than younger patients. Consequently, the level of eGFR below which the risk of ESRD exceeded the risk of death varied by age, ranging from 45 ml/min per 1.73 m(2) for 18 to 44 year old patients to 15 ml/min per 1.73 m(2) for 65 to 84 year old patients. Among those 85 years or older, the risk of death always exceeded the risk of ESRD in this cohort. Among patients with eGFR levels <45 ml/min per 1.73 m(2) at baseline, older patients were less likely than their younger counterparts to experience an annual decline in eGFR of >3 ml/min per 1.73 m(2). In conclusion, age is a major effect modifier among patients with an eGFR of <60 ml/min per 1.73 m(2), challenging us to move beyond a uniform stage-based approach to managing CKD.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".