Has the time come for age-adapted glomerular filtration rate criteria to define chronic kidney disease: how soon is now?
Bibliographic record
Abstract
PURPOSE OF REVIEW: The conventional definition of chronic kidney disease (CKD) primarily relies on the identification of albuminuria or a decline in estimated glomerular filtration rate (eGFR). For many years, a straightforward eGFR threshold of <60 ml/min/1.73 m 2 has been widely adopted as the standard for defining CKD. Nonetheless, this criterion fails to consider the natural aging process of the kidney, and this oversight may affect the accurate diagnosis of kidney disease particularly at the extremes of age. RECENT FINDINGS: The fixed eGFR threshold of <60 ml/min/1.73 m 2 for defining CKD misses crucial opportunities for risk prevention. Studies have revealed that the eGFR threshold at which the risks for adverse long-term health outcomes such as mortality, cardiovascular events, and kidney failure begin to rise varies substantially by age. Specifically, this threshold is lower for the elderly and higher for young adults. Consequently, this results in the over-diagnosis of kidney disease in the elderly and the under-diagnosis of kidney disease in young adults. SUMMARY: To address these limitations of the current CKD definition, we discuss a number of proposed age-adapted eGFR criteria and weigh their pros and cons against the current, simple, and universally accepted approach.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".