Estimation of GFR Using β-Trace Protein in Children
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Sex may affect the performance of small molecular weight proteins as markers of GFR because of differences in fat mass between the two sexes. The hypothesis was that the diagnostic performance of β-trace protein, a novel marker of GFR, would be significantly better in boys than in girls. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: GFR, height, weight, serum creatinine, and β-trace protein were measured in 755 children and adolescents (331 girls) undergoing (99)technetium diethylenetriamine penta-acetic acid renal scans from July of 1999 to July of 2006. Boys and girls were separated into formula generation cohorts (284 boys and 220 girls) and formula validation cohorts (140 boys and 111 girls). GFR-estimating formulas on the basis of β-trace protein, creatinine, and height were derived using stepwise linear regression analysis of log-transformed data. The slope of the regression lines of the sex-specific eGFRs were compared. Bland-Altman analysis was used for testing agreement between (99)technetium diethylenetriamine penta-acetic acid GFR and calculated GFR both with this equation in boys and girls as well as previously established Benlamri, White, and Schwartz formulas. RESULTS: In the stepwise regression analysis, β-trace protein (R(2)=0.73 for boys and R(2)=0.65 for girls) was more important than creatinine (which increased R(2) to 0.81 for boys and R(2) to 0.75 for girls) and height (which increased R(2) to 0.88 for boys and R(2) to 0.80 for girls) in the data generation groups. GFR can be calculated using the following formulas:[Formula: see text]and[Formula: see text]Bland-Altman analysis showed better performance in boys than in girls. The new formulas performed significantly better than the previous Benlamri, White, and Schwartz formulas with respect to bias, precision, and accuracy. CONCLUSIONS: Improved and sex-specific formulas for the estimation of GFR in children on the basis of β-trace protein, serum creatinine, and height are now available.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".