Practice Patterns of Nephrologists Who Care for Pregnant Patients with CKD
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) increases adverse pregnancy events such as pre-eclampsia, preterm delivery, and progression of maternal CKD. While live birth rates among pregnant people with kidney disease have increased, these pregnancies remain a high risk, emphasizing the critical need for advanced understanding and management strategies. Data on Canadian nephrologist practice patterns and center-specific strategies to improve outcomes for pregnant people with CKD are limited. This study used survey methodology to assess practice patterns and policies in this area. Methods: We conducted a national, cross-sectional survey to assess the practice patterns of Canadian nephrologists caring for pregnant people with CKD. Following literature review, we developed a list of items covering key aspects of CKD management in people who are pregnant, including pre-conception counseling, multispecialty team collaboration and post-transplant care. Items were refined and deduplicated through iterative review by team members. The survey was distributed through professional networks. The responses were analyzed descriptively and key findings were presented as percentages. Additionally, the results for each aspect of the questionnaire were evaluated in light of existing recommendations. Results: The survey response rate was 71% (25/35). Of the responding nephrologists, 76% identified as women, and 52% had been in practice between 10 and 19 years. Regarding multispecialty care, 36% reported having a full team, 40% had some team elements, and 24% had no team. Individualization of patient care was the most common practice, as opposed to a conventional or standardized approach. This preference for individualization extended to offering pre-natal genetic counselling, antenatal kidney biopsy and antibiotic prophylaxis, and post-partum ACE inhibitor re-initiation. Conclusions: Our study is the first to assess the practice patterns and policies of Canadian nephrologists caring for pregnant people with CKD. We found important variations in obstetric nephrology care, and that the majority of programs did not have a full multispecialty team. These results emphasize an opportunity to improve care through the creation of formal multidisciplinary teams and the consistent adoption of evidence-based policies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".