Inherited kidney disease and CAKUT are common causes of kidney failure requiring kidney replacement therapy: an ERA Registry study
Bibliographic record
Abstract
BACKGROUND: Inherited kidney diseases (IKDs) and congenital anomalies of the kidney and urinary tract (CAKUT) are causes of kidney failure requiring kidney replacement therapy (KRT) that major renal registries usually amalgamate into the primary renal disease(PRD) category 'miscellaneous' or in the glomerulonephritis or pyelonephritis categories. This makes IKDs invisible (except for polycystic kidney disease) and may negatively influence the use of genetic testing, which may identify a cause for IKDs and some CAKUT. METHODS: We re-examined the aetiology of KRT by composing a separate IKD and CAKUT PRD group using data from the European Renal Association (ERA) Registry. RESULTS: In 2019, IKD-CAKUT was the fourth most common cause of kidney failure among incident KRT patients, accounting for 8.9% of cases [IKD 7.4% (including 5.0% autosomal dominant polycystic kidney disease), CAKUT 1.5%], behind diabetes (23.0%), hypertension (14.4%) and glomerulonephritis (10.6%). IKD-CAKUT was the most common cause of kidney failure among patients <20 years of age (41.0% of cases), but their incidence rate was highest among those ages 45-74 years (22.5 per million age-related population). Among prevalent KRT patients, IKD-CAKUT (18.5%) and glomerulonephritis (18.7%) were the two most common causes of kidney failure overall, while IKD-CAKUT was the most common cause in women (21.6%) and in patients <45 years of age (29.1%). CONCLUSION: IKD and CAKUT are common causes of kidney failure among KRT patients. Distinct categorization of IKD and CAKUT better characterizes the epidemiology of the causes of chronic kidney disease (CKD) and highlights the importance of genetic testing in the diagnostic workup of 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".