Use of the kidney failure risk equation: a regional retrospective primary care cohort study in England
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is associated with increased risk of death and progression to kidney failure requiring renal replacement therapy (RRT). Predicting those at greatest risk of RRT is essential for effective clinical care. The kidney failure risk equation (KFRE) is useful for predicting risk of RRT but does not consider the competing risk of death. AIM: To measure the association between KFRE scores and probability of death versus RRT; and to quantify the number of patients with a KFRE score >5%. DESIGN AND SETTING: Retrospective cohort study conducted from 2018 to 2023 using the primary care Greater Manchester Care Record. METHOD: A mixed descriptive and regression analyses. Multinomial regression was used to measure the association between KFRE score categories (<5%, 5%-20%, and >20%) and relative risk/probability of death and RRT. RESULTS: In general, for the KFRE category of <5%: probability of RRT was 0.1% (95% confidence interval [CI] = 0.1 to 0.2) and death was 11.9% (95% CI = 11 to 13); for the KFRE category 5%-20%: probability of RRT was 2% (95% CI = 1 to 3) and death was 23% (95% CI = 20 to 27); and for the KFRE category of >20%: probability of RRT was 14% (95% CI = 8 to 23) and death was 29% (95% CI = 25 to 36). On average, 11% of patients with CKD stages 3-5 had a KFRE score >5% and could be eligible for referral to nephrology. CONCLUSION: The probability of death was generally greater than RRT across KFRE categories - useful for clinicians to consider in shared decision making and management. An estimate of all patients potentially eligible for referral to nephrology is useful for care delivery and provision of nephrology services.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".