Sex-Related Differences in CKD Monitoring and Cardiovascular Risk Management in Australian Primary Care: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: There has been limited assessment of sex-related differences in chronic kidney disease (CKD) management. We aimed to explore sex-related differences in monitoring and cardiovascular risk management of CKD in primary care. Methods: We identified adults with CKD who attended a general practice participating in MedicineInsight (2011-2020). Sex differences in monitoring and management were assessed within 18 months of meeting diagnostic CKD criteria. Core monitoring was defined as ≥1 measurement of blood pressure, eGFR, UACR, lipids and, in diabetics, HbA1c. Cardiovascular risk management comprised ACEi/ARB and statin prescriptions, blood pressure and lipid control. Adjusted modified Poisson regression determined the relative risk (RR) of outcomes in females vs. males (overall and within subgroups [age, comorbidities and CKD risk categories]). Results: Of 140,774 patients with CKD, 51.4% were female. Females were older (mean age: 75.8 vs. 72.7 years) and had less prevalent CVD and diabetes. Females were less likely than males to receive core monitoring (RR [95% CI], 0.96 [0.95-0.98]), ACEi/ARB prescription (0.96 [0.95-0.97]; no difference in statin prescription), blood pressure targets (<140/90mmHg: 0.96 [0.95-0.97]) and LDL <2mmol/L (0.82 [0.80-0.84]). Females with advancing age, co-existing CVD, diabetes or hypertension, and moderately increased and high-risk CKD were less likely to be monitored (Figure 1). Conclusion: Overall, females with CKD were less likely to receive CKD monitoring and cardiovascular risk management. Findings largely persisted in advancing age, comorbidity and CKD risk categories. Funding: Commercial Support - The Renal Division of The George Institute for Global Health has received sponsorship funding provided by Boehringer Ingelheim and Eli Lilly Alliance and is supported by the University of New South Wales Scientia Program. The design, analysis, interpretation or writing of this work was performed independent of all funding bodies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".