Reproductive Health and CKD: A Cross-Sectional Survey of Patient Knowledge and Educational Needs
Bibliographic record
Abstract
Background: Female reproductive health is affected by kidney disease, but is often not addressed in nephrology care. Our objective was to better understand patient educational needs on this topic to better facilitate family planning care for people with CKD. Methods: We conducted an online survey of people with CKD assigned female sex at birth, aged 18-45, in English. Survey topics included current knowledge satisfaction, self-ranked education needs, communication preferences, health history, and demographics. Univariate(UV) and multivariable(MV) logistic regression were used to determine associations between individual patient characteristics and overall knowledge satisfaction. Results: 209 surveys were completed; 77% of participants self-identified as white, 11% Black, 4% Asian; 11% Hispanic. 23% had limited health literacy. Many were dissatisfied with their knowledge of contraception and pregnancy outcomes with CKD (49% and 36%, respectively). Pregnancy planning was associated with lower knowledge satisfaction and glomerular disease with higher knowledge satisfaction. However, after adjustment in MV analysis only health literacy was significantly associated with knowledge satisfaction (β -0.5, 95% CI -0.9 to -0.02) (Table 1). Understanding CKD impact on fetal development and menstruation, and kidney function changes after pregnancy were topics ranked as high priority by participants. The majority were open to receiving a recommendation about contraception (76%; n=159/209) or pregnancy timing (77%; n=161/209) from their nephrologist. Conclusion: These findings suggest priority topics that should be included in family planning during CKD care. Patients are open to receiving this advice in nephrology clinics. Findings also reinforce the need for education tools to address family planning for all patients, including those with limited health literacy. Funding: NIDDK Support UV and MV associations of patient characteristics with knowledge satisfaction about reproductive health and CKD - Patient characteristics Estimated univariate effect 95% CI P Estimated adjusted effect* 95% CI Adjusted P Multigravid vs nulligravid 0.2 -0.1 – 0.5 0.17 0.04 -0.4 – 0.4 0.86 Planning a pregnancy vs not or unsure -0.4 -0.7 - -0.1 0.009 -0.3 -0.7 – 0.1 0.19 Stage 1-2 vs stage 3-5 CKD 0.2 -0.04 – 0.5 0.10 0.2 -0.2 – 0.5 0.37 Glomerular disease vs not glomerular disease etiology 0.3 0.01 – 0.5 0.043 0.3 -0.1 – 0.6 0.12 Additional year of age 0.02 -0.002 – 0.03 0.087 0.002 -0.03 – 0.03 0.90 Assistance with reading hospital materials (ever vs never) -0.2 -0.6 – 0.1 0.16 -0.5 -0.9 - -0.02 0.043 *Adjusted model included race, ethnicity, educational attainment, and income
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".