Cystatin‐C and beta trace protein as markers of renal function in pregnancy
Bibliographic record
Abstract
OBJECTIVE: To assess the validity of Cystatin-C (Cys-C) and beta trace protein (BTP) as clinical markers of glomerular filtration rate (GFR) in pregnant women. DESIGN: Prospective cross sectional study. SETTING: Obstetric unit of a tertiary care hospital. POPULATION: One hundred and thirty-seven normal pregnant women and 13 women postpartum. METHODS: Twenty-four hour creatinine clearance (CrCl), serum creatinine, Cys-C and BTP concentrations were measured on normal pregnant women in the first trimester (n= 5), second trimester (n= 68) and third trimester (n= 64) and in 13 women postpartum. Data are given as median (2.5th centile, 97.5th centile). MAIN OUTCOME MEASURES: Serum concentrations of Cys-C and BTP compared with creatinine clearance and serum creatinine. RESULTS: The median serum creatinine throughout gestation was 53 micromol/L (39, 71), and median CrCl was 143 mL/minute (91 to 216). Postpartum, creatinine rose to 74 micromol/L (58, 86) and CrCl decreased to 104 mL/minute (71, 159). For Cys-C, the median concentration was 0.70 mg/L (0.46, 1.32), and 0.54 mg/L (0.36, 0.96) for BTP. Comparing the second and third trimesters, there was no significant difference between CrCl (median 145 vs 141 mL/minute) and BTP concentrations (median 0.51 vs 0.55 mg/L), while median Cys-C was significantly higher in the third trimester (0.61 vs 0.88 mg/L; P < 0.001). Unlike creatinine and BTP, Cys-C levels decreased to 0.72 mg/L (0.57, 0.95) postpartum. The only significant relationship of either of these markers to the standard used for GFR was between Cys-C and CrCl in the third trimester, and the correlation was weak (r= 0.27 for 1/Cys-C vs CrCl). CONCLUSION: These data demonstrate that despite claims to the contrary, Cys-C is a poor marker of GFR during pregnancy. Similarly, BTP shows little promise.
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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 | 0.002 | 0.011 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".