Kidney length normative values in children aged 0–19 years — a multicenter study
Bibliographic record
Abstract
Abstract Background Currently used pediatric kidney length normative values are based on small single-center studies, do not include kidney function assessment, and focus mostly on newborns and infants. We aimed to develop ultrasound-based kidney length normative values derived from a large group of European Caucasian children with normal kidney function. Methods Out of 1,782 children aged 0–19 years, 1,758 individuals with no present or past kidney disease and normal estimated glomerular filtration rate had sonographic assessment of kidney length. The results were correlated with anthropometric parameters and estimated glomerular filtration rate. Kidney length was correlated with age, height, body surface area, and body mass index. Height-related kidney length curves and table were generated using the LMS method. Multivariate regression analysis with collinearity checks was used to evaluate kidney length predictors. Results There was no significant difference in kidney size in relation to height between boys and girls. We found significant ( p < 0.001), but clinically unimportant (Cohen’s D effect size = 0.04 and 0.06) differences between prone vs. supine position (mean paired difference = 0.64 mm, 95% CI = 0.49–0.77) and left vs. right kidneys (mean paired difference = 1.03 mm, 95% CI = 0.83–1.21), respectively. For kidney length prediction, the highest coefficient correlation was observed with height (adjusted R 2 = 0.87, p < 0.0001). Conclusions We present height-related LMS-percentile curves and tables of kidney length which may serve as normative values for kidney length in children from birth to 19 years of age. The most significant predictor of kidney length was statural height. Graphic Abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".