Circulating Angiogenic Factors in a Pregnant Woman on Intensive Hemodialysis: A Case Report
Bibliographic record
Abstract
BACKGROUND: Pregnancy in patients on chronic hemodialysis therapy, though unlikely, does happen rarely. Intensive hemodialysis is thought to offer a better survival advantage to the unborn child. Circulating angiogenic factors are helpful for prognostication of pregnant patients with chronic kidney disease who are not on dialysis. Data on their utilization in dialysis patients, however, are limited. CASE PRESENTATION: We report the case of a patient with a history of interstitial nephritis who had a kidney transplant that failed after 8 years due to membranous nephropathy. She was initiated on hemodialysis three sessions per week and conceived after being on dialysis for 6 weeks. She was switched to intensive hemodialysis at 8 weeks of gestation and had a C-section because of hypertension at 35 weeks, with delivery of a healthy girl weighing 2012 g. Serum angiogenic factors (placental growth factor and soluble fms-like tyrosine kinase) were measured at 32, 33, and 34 weeks of gestation and at 1, 2, and 3 weeks postpartum. Serum angiogenic factors were similar to what has been reported for patients with chronic kidney disease and were not consistent with preeclampsia. CONCLUSIONS: Our case report expands on the literature regarding intensive hemodialysis and angiogenic factor utilization in pregnant dialysis patients. Our case report suggests that starting intensive dialysis early in pregnancy is safe and concentration of angiogenic factors are similar to those reported for patients without kidney disease, except for PIGF levels, which are somewhat higher.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| 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".