Serum Creatinine Levels Before, During, and After Pregnancy
Bibliographic record
Abstract
Serum Creatinine Levels Before, During, and After PregnancyEstimating renal function before and during pregnancy has clinical importance: kidney dysfunction can affect maternal and perinatal health.Glomerular hyperfiltration is a typical physiological adaptation to pregnancy, reflected by a decrease in levels of serum creatinine (SCr) with advancing gestational age.Creatinine-based equations used to estimate glomerular filtration may misclassify renal function during pregnancy, 1 as they depend on a steady state of creatinine balance.Moreover, a 24-hour collection of urine to measure cre-atinine clearance is impractical.2 Accordingly, physicians typically rely on SCr level.Previous studies attempted to define a normal SCr level in pregnancy, but they had few participants and may have been confounded by sampling bias.3,4 The current study was undertaken to generate gestational age-specific estimates of renal function-before, during, and after pregnancy-among women without antecedent kidney disease.A, Dashed curves indicate upper and lower 95% CI bounds.B, Values adjacent to each curve indicate the percentile-specific corresponding serum creatinine values at each time point.To convert creatinine values to mg/dL, divide by 88.4.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".