Risk Prediction for Chronic Kidney Disease: Considerations for Primary Care
Bibliographic record
Abstract
Chronic Kidney Disease (CKD) affects more than one in ten Canadians and is largely managed in primary care. Diabetes is the leading cause of CKD, and primary care providers often manage the underlying causes and comorbid conditions related to kidney disease, as well as the adverse consequences of CKD itself. It is important to recognize that CKD has a variable course. While most adults lose approximately 1 mL/min of kidney function every year after the age of 40, some patients lose kidney function rapidly, leading to hospitalizations due to heart failure and progression to kidney failure, whereas others remain stable for decades, requiring minimal additional intervention. Recent advances in risk prediction for CKD allows all providers to accurately identify high risk individuals. These innovations enable the use of highly effective therapies that slow down, and in many cases, normalize the rate of kidney function loss, leading to potential lifetime risk reduction for kidney failure. (Figure 1). This review will cover key considerations in screening, risk stratification, and treatment of CKD in primary care, with an emphasis on tools that are readily available in clinical settings. We believe that a screen-triage-treat paradigm for CKD can lead to optimal outcomes for patients and health systems.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".