Advanced chronic kidney disease among UK children
Bibliographic record
Abstract
The UK Renal Registry currently collects information on UK children with kidney failure requiring long-term kidney replacement therapy (KRT), which supports disease surveillance and auditing of care and outcomes; however, data are limited on children with chronic kidney disease (CKD) not on KRT. Methods In March 2020, all UK Paediatric Nephrology centres submitted data on children aged <16 years with severely reduced kidney function as of December 2019, defined as an estimated glomerular filtration rate <30 mL/min/1.73 m2. Results In total, 1031 children had severe CKD, the majority of whom (80.7%) were on KRT. The overall prevalence was 81.2 (95% CI 76.3 to 86.3) per million of the age-related population. Conclusions The prevalence of severe CKD among UK children is largely due to a high proportion of children on long-term KRT. Expanding data capture to include children with CKD before reaching failure will provide greater understanding of the CKD burden in childhood.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".