Bibliographic record
Abstract
Glomerular hyperfiltration, altered tubular function, and shifts in electrolyte-fluid balance are among the hallmark renal physiologic changes that characterize a healthy pregnancy. These adjustments are not only critical to maternal and fetal well being, but also provide the clinical context for identifying gestational aberrations in renal function and electrolyte composition. Systemic vasodilation characterizes early gestation and produces increments in renal plasma flow and GFR, the latter of which is maintained into the postpartum period. In addition, renal tubular changes allow for the accumulation of nutrients and electrolytes necessary for fetal growth such that wasting of proteins, glucose, and amino acids in urine is limited in pregnancy and total body stores of electrolytes increase throughout gestation. Substantial insight into the mechanisms underlying these complex adjustments can be gleaned from the available animal and human literature, but our understanding in many areas remains incomplete. This article reviews the available literature on renal adaptation to normal pregnancy, including renal function, tubular function, and electrolyte-fluid balance, along with the clinical ramifications of these adjustments, the limitations of the existing literature, and suggestions for future studies.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".