Women and kidney health: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference
Bibliographic record
Abstract
The KDIGO (Kidney Disease: Improving Global Outcomes) Controversies Conference on Women and Kidney Health was convened to identify key sex and gender issues in kidney care, practices for optimizing healthcare in women with kidney diseases, and priorities for future research. Participants emphasized the importance of addressing the influence of sex and gender in diagnosis, risk assessment, prognosis, and treatment of chronic kidney disease (CKD) and its complications, as well as considering issues across the lifespan (puberty, sexual and reproductive health, menopause). CKD is a risk factor for adverse pregnancy outcomes with every type of kidney disease and severity. All women of reproductive age known to have CKD should be counseled on contraception, the ideal timing of pregnancy, the risks and outcomes for mother and fetus, fertility treatments where these are available, medication management, and medical aspects of pregnancy termination. A successful pregnancy is possible across all severities of CKD, including in women living with dialysis or a kidney transplant. Pregnancy should be managed with a multidisciplinary care plan based upon the type of kidney disease and the presence and severity of kidney function impairment, hypertension, and proteinuria. Systematic assessment of blood pressure, proteinuria, and kidney function in all pregnancies would facilitate diagnosis of CKD and detection of acute kidney injury (AKI). Follow-up programs for women who experienced pregnancy-related AKI, preeclampsia, or other hypertensive disorders of pregnancy are important as these conditions may reflect undiagnosed CKD and have important implications for future cardiovascular health.
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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 | 0.050 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".