Bibliographic record
Abstract
Chronic kidney disease represents a heterogeneous group of disorders characterized by alterations in the structure and function of the kidney. Chronic kidney disease significantly increases the risk of adverse maternal and perinatal outcomes, and these risks increase with the severity of the underlying renal dysfunction, degree of proteinuria, as well as the frequent coexistence of hypertension. Further, renal anatomic changes result in dilatation of the collecting system, and physiologic adaptations include alterations in the balance of vasodilatory and vasoconstrictive hormones, resulting in decreased systemic and renal vascular resistance, increased glomerular filtration rate, and modifications in tubular function. These alterations have important clinical implications and can make the diagnosis of renal compromise challenging. The effect of pregnancy on kidney disease may manifest as a loss of renal function, particularly in the context of concomitant hypertension and proteinuria, and chronic kidney disease, even when mild, contributes to the high risk of adverse pregnancy outcomes, including increased risks of preeclampsia, preterm delivery, and small-for-gestational age neonates. Strategies for optimization of pregnancy outcomes include meticulous management of hypertension and proteinuria where possible and the initiation of preeclampsia prevention strategies, including aspirin. Avoidance of nephrotoxic and teratogenic medications is necessary, and renal dosing of commonly used medications must also be considered. Mode of delivery in women with chronic kidney disease should be based on usual obstetric indications, although more frequent prenatal assessments by an expert multidisciplinary team are desirable for the care of this particularly vulnerable patient population. Obstetricians represent a critical component of this team responsible for managing each stage of pregnancy to optimize both maternal and neonatal outcomes, but collaboration with nephrology colleagues in combined clinics wherein both specialists can make joint management decisions is typically very helpful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".