Can we identify adolescents at high risk for nephropathy before the development of microalbuminuria?
Bibliographic record
Abstract
AIMS: To determine whether higher than average albumin excretion during early puberty identifies subjects who will subsequently develop microalbuminuria (MA) and clinical proteinuria. METHODS: Longitudinal data from the Oxford Regional Prospective Study of Childhood Diabetes (ORPS; n = 554, median duration of follow-up 10 years; range 3.0-16.7) with assessment of albumin/creatinine ratios in three early morning urine samples collected annually. An albumin excretion phenotype was derived from longitudinal data, for each individual, defining deviation from the mean of regression models, including covariates gender, age, duration of diabetes and age at assessment. Tracking of the phenotypes was confirmed in a second independent cohort from Perth, Australia. RESULTS: The albumin excretion phenotype showed reasonable correlation between age 11-15 years and age 16-18 years in both cohorts, indicative of good 'tracking'. In the ORPS cohort, tertiles of the albumin excretion phenotype at aged 11-15 years were predictive of subsequent risk for the development of MA. All of the subjects developing clinical proteinuria had an albumin excretion phenotype in the upper tertile or an HbA(1c) > 9% at aged 11-15 years. CONCLUSIONS: Identification of adolescents at risk of diabetic nephropathy using an albumin excretion phenotype is feasible. When combined with elevated HbA(1c), it may identify subjects for trial of early intervention with angiotensin-converting enzyme inhibitors/angiotensin-II receptor antagonists and statins to improve long-term prognosis in these subjects where sustained improvement in glycaemic control may be difficult to achieve.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".