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Record W4417397542 · doi:10.1016/j.xkme.2025.101222

Reproductive Health Knowledge and Educational Priorities in Chronic Kidney Disease: A Cross-Sectional Survey of People With CKD

2025· article· en· W4417397542 on OpenAlexaff
Kelcie K. Darpel, Julie Wright-Nunes, Sarah T. Hawley, Michelle Hladunewich, Claire Z. Kalpakjian, Corey Powell, Andrea L. Oliverio

Bibliographic record

VenueKidney Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicPregnancy and Medication Impact
Canadian institutionsSunnybrook Health Science Centre
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institutes of HealthNational Kidney Foundation
KeywordsReproductive healthPregnancyKidney diseaseMEDLINEReproductive EndocrinologyPopulation

Abstract

fetched live from OpenAlex

Rationale & Objective Female reproductive health is affected by kidney disease but is often not addressed in nephrology care. Our objective was to better understand patient education needs on this topic to facilitate reproductive health education and care in chronic kidney disease (CKD). Study Design A cross-sectional online survey. Setting & Participants Female assigned sex at birth, aged 18-45 years, within the United States, fluent in English, with CKD recruited from national kidney organizations, research consortiums, and an academic medical center. Outcomes Reproductive health knowledge satisfaction, education needs, and communication preferences and barriers. Exposures Gravidity, pregnancy planning, CKD stage, disease etiology, health literacy, and demographics. Analytic Approach Univariate and multivariable logistic regression were used to examine associations between patient characteristics, knowledge satisfaction, and education needs. Descriptive statistics were used to assess communication preferences and barriers. Results Two hundred and nine surveys were completed. In total, 77% of participants self-identified as White, 11% Black, 4% Asian, and 11% Hispanic. A total of 23% had limited health literacy. Individuals planning a pregnancy in the future had lower knowledge satisfaction in univariate analysis. After multivariable analysis, only health literacy was significantly associated with knowledge satisfaction ( β , −0.5; 95% CI, −0.9 to −0.02; P = 0.04). Understanding the impact of CKD on fetal development and menstruation, and kidney function changes after pregnancy were topics ranked as high priority by patients. Most wanted a nephrologist's recommendation about birth control (76%; n=159/209) and pregnancy timing (77%; n=161/209). Limitations The limitations include convenience sampling and generalizability because of the online delivery of the survey and overrepresentation of higher socioeconomic groups. Conclusions This study provides priority topics to include in pregnancy planning during CKD care. Patients want advice from their nephrologists. More tools are needed to support reproductive health education for people with CKD, starting by addressing the need for those with limited health literacy. Plain-language Summary Chronic kidney disease (CKD) presents reproductive health challenges that can affect both the mother and fetus. Our study surveyed 209 women with CKD to assess their current reproductive health knowledge satisfaction, educational needs, and communication preferences and barriers. Results revealed that women are not satisfied with their knowledge about the effects of CKD on reproductive health, especially for those planning pregnancies or with low health literacy. Opportunities for nephrologists to help counsel patients on reproductive health include the effect of CKD on fetal development and disease progression after pregnancy. Despite some communication barriers, patients are open to nephrologist recommendation on reproductive health. Educational tools and nephrologist-patient discussions could improve shared decision making and improve reproductive outcomes for CKD patients.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.381
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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