A Novel Equation to Estimate Glomerular Filtration Rate Using Beta-Trace Protein
Bibliographic record
Abstract
BACKGROUND: Beta-trace protein (BTP) is a low molecular weight glycoprotein that is a more sensitive marker of glomerular filtration rate (GFR) than serum creatinine. The utility of BTP has been limited by the lack of an equation to translate BTP into an estimate of GFR. The objectives of this study were to develop a BTP-based GFR estimation equation. METHODS: We measured BTP and GFR by (99m)technetium-diethylenetriaminepentaacetic acid in 163 stable adult renal transplant recipients. Stepwise multiple regression models were created to predict GFR corrected for body surface area. The following variables were considered for entry into the model: BTP, urea, sex, albumin, creatinine, age, and race. RESULTS: BTP alone accounted for 75.6% of variability in GFR. The model that included all the predictor variables had the largest coefficient of determination (R(2)) at 0.821. The model with only BTP, urea, and sex had only a slightly lower R(2) of 0.81 and yielded the following equation: GFR mL . min(-1) . (1.73 m(2))(-1) = 112.1 x BTP(-0.662) x Urea(-0.280) x (0.88 if female). A 2nd equation (R(2) = 0.79) using creatinine instead of urea was also developed: GFR mL . min(-1) . (1.73 m(2))(-1) = 1.678 x BTP(-0.758) x creatinine(-0.204) x (0.871 if female). CONCLUSIONS: We have shown that BTP can be used in a simple equation to estimate GFR. Further study is needed in other populations to determine accuracy and clinical utility of this equation.
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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 | 0.003 | 0.010 |
| 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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".