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Record W2104345347 · doi:10.1093/ndt/gfn505

Body mass does not have a clinically relevant effect on cystatin C eGFR in children

2008· article· en· W2104345347 on OpenAlexaff
Ajitha Sharma, Anusha Kathiravelu, Ravichandran Nadarajah, A. Yasin, Guido Filler

Bibliographic record

VenueNephrology Dialysis Transplantation · 2008
Typearticle
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of Ottawa
Fundersnot available
KeywordsCystatin CMedicineRenal functionBody mass indexLean body massKidney diseaseCreatinineBody surface areaUrologyInternal medicineEndocrinologyBody weight

Abstract

fetched live from OpenAlex

BACKGROUND: Unlike creatinine, Cystatin C (CysC) is believed to be independent of body composition in both adults and children. Recent findings in adults, suggesting an improved performance of CysC-based estimated glomerular filtration rate (CysC eGFR) by accounting for body mass, necessitated a careful re-evaluation of this issue in children. METHODS: We studied 240 children (median age 11.7 years, range 2-17.9 years, 107 girls), with various kidney diseases, for any change in the relationship between (99)Tc DTPA GFR and CysC eGFR after accounting for body mass. For body mass assessment, body mass index (BMI) z-score was calculated using height-adjusted age, to account for growth retardation secondary to chronic kidney disease. RESULTS: CysC eGFR did not have a significant correlation with BMI z-score (correlation coefficient = 0.06; P = 0.34). Accounting for BMI z-score did not add to the 65% variance in nuclear GFR explained by CysC eGFR. Moreover, it did not change the regression coefficient of 0.85 between CysC eGFR and nuclear GFR either. On Bland & Altman analysis, the bias of 0.05 and standard deviation of 20.39 also did not improve after accounting for BMI z-score in the revised CysC eGFR formula. CONCLUSIONS: In children, body mass exerts a minimal effect on the performance of CysC eGFR estimation.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.265
Teacher spread0.257 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2008
Admission routes1
Has abstractyes

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