Renal length z‐score for the detection of dysfunction in children with solitary functioning kidney
Bibliographic record
Abstract
AIM: To evaluate whether renal length z-scores predict renal dysfunction in children with a solitary functioning kidney (SFK). METHODS: In a single-centre retrospective cohort of children with SFK, we correlated body mass index z-scores, extracellular volume and lean body mass to renal length z-scores. We grouped these z-scores to other markers of renal dysfunction (proteinuria, hypertension, extracellular volume and abnormal estimated glomerular function rate [eGFR]) and analysed renal length z-score with multivariate analysis, receiver-operated characteristics (ROC) plots and Youden's index to determine an appropriate cut-off. RESULTS: , and age at last follow-up 7.4 (3.8-13.4 years). The median renal length z-scores of those without any renal dysfunction (n = 37, 25.1%) were greater (+3.66, interquartile range 3.02-4.47) than those with renal dysfunction (median 3.11, interquartile range 1.76-4.11, P = .0107, Mann-Whitney test). Using a cut-off of z-score of >+1.911, the odds ratio for having no renal dysfunction was 0.07 (95% CI 0.002-0.459, P = .0010). However, accuracy of the renal length z-score was poor (ROC curve 0.6488). CONCLUSION: In this cohort of children with SKF, using the renal length z-score as a biomarker of renal dysfunction at 7 years of age is not recommended.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".