Gender Differences in CKD Progression: Real-World Data (RWD) Analysis
Bibliographic record
Abstract
Background: Large population-based studies for CKD progression are scant. RWD may better predict population trends for dialysis initiation by gender. Methods: HealthVerity PrivateSource20 closed claims linked with Veradigm Health Insights EHR and Quest laboratory results data of adults with >364 days of continuous enrollment between 1/1/2017 and 11/30/2021 compared progression of CKD stages ≥3b to dialysis, by gender. Patients were required to have ≥2 eGFR measurements 90-365 days apart and followed until they initiated dialysis or end of available data with the first eGFR as the index date. We excluded pregnancy, AKI, ESKD and dialysis during the baseline period. Covariates included gender, country region, age at index date, Deyo-Chronic Comorbidity Index (CCI) Score, eGFRs, payer types, and comorbidities. Results: 14,172 met the study criteria (Table) with a higher proportion of women patients across all stages (p<0.04). Mean (SD) eGFR test results upon cohort entry were clinically similar for men (34.1±8.0) and women (33.8±8.1). The type of insurance differed between men and women (p<0.05) with men more likely to have commercial insurance and women more likely to have Medicaid with no differences in region. Mean Deyo-CCI scores were significantly (p<0.05) higher for men compared to women and a higher proportion of men had osteodystrophy (2.2% vs 1.6%, p = 0.007) while women were more likely to have anemia (21% vs 19.6%, p<0.05). For CKD stages ≥3b, the proportion of men who initiated dialysis was significantly higher (2.5% vs 1.9%, p<0.05), and the mean time to initiation of dialysis was significantly shorter (510±340 vs 530±356 days, p<0.05) compared to women. These results were primarily driven by patients with stage 4 CKD. Conclusions: RWD confirm that CKD prevalence was higher among women while progression to dialysis was only mildly faster among men. Insurance classes, comorbidity scores, anemia and osteodystrophy rates between genders were found to be significantly different. Funding: Commercial Support - ProKidney, LLCCKD Progression to Dialysis
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".