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Record W3206880034 · doi:10.1007/s00467-021-05303-5

Kidney length normative values in children aged 0–19 years — a multicenter study

2021· article· en· W3206880034 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePediatric Nephrology · 2021
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsChildren's Hospital of Eastern Ontario
Fundersnot available
KeywordsMedicineRenal functionPercentileBody surface areaKidneyKidney diseaseAnthropometryNephrologyInternal medicineUrologyStatisticsMathematics

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.270
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it