The Prevalence of CKD in Australian Primary Care: Analysis of a National General Practice Dataset
Bibliographic record
Abstract
Introduction: The prevalence of chronic kidney disease (CKD) in Australia varies substantially across reports. Using a large, nationally representative general practice data source, we determined the contemporary prevalence and staging of CKD in the Australian primary care. Methods: We performed a retrospective, community-based observational study of 2,720,529 adults with ≥1 visit to a general practice participating in the MedicineInsight program and ≥1 serum creatinine measurement (with or without a urine albumin-to-creatinine ratio [UACR] measurement) between 2011 and 2020. CKD prevalence was estimated using 3 definitions based on estimated glomerular filtration rate (eGFR) and UACR measurements with varying degrees of rigidity in terms of the number of measurements assessed to define CKD ("least", "moderate" and "most" rigid). Results: CKD prevalence in the cohort progressively increased over the 10-year study period, irrespective of the method used to define CKD. In 2020, CKD prevalence in the cohort was 8.4%, 4.7%, and 3.1% using the least, moderate, and most rigid definition, respectively. The number of patients with UACR measurements was low such that, among those with CKD in 2020, only 3.8%, 3.2%, and 1.5%, respectively, had both eGFR and UACR measurements available in the corresponding year. Patients in whom both eGFR and UACR measurements were available mostly had moderate or high risk of CKD progression (83.6%, 80.6%, and 76.2%, respectively). Conclusion: In this large, nationally representative study, we observed an increasing trend in CKD prevalence in primary care settings in Australia. Most patients with CKD were at moderate to high risk of CKD progression. These findings highlight the need for early detection and effective management to slow progression of CKD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".